Instance-Level Composed Image Retrieval
Authors: Bill Psomas
George Retsinas
Nikos Efthymiadis
Panagiotis Filntisis
Yannis Avrithis
Petros Maragos
Ondrej Chum
Giorgos Tolias
Venue: NeurIPS 2025 poster ()
Keywords: Image Retrieval
Composed Image Retrieval
Vision-Language Models
Transformers
Instance-Level
Primary area: applications
TL;DR: We introduce i-CIR—an instance‐level composed image retrieval benchmark with rigorously curated hard negatives—and BASIC, a training‐free VLM‐based method that centers and projects image embeddings.

Abstract: The progress of composed image retrieval (CIR), a popular research direction in image retrieval, where a combined visual and textual query is used, is held back by the absence of high-quality training and evaluation data. We introduce a new evaluation dataset, i-CIR, which, unlike existing datasets, focuses on an instance-level class definition. The goal is to retrieve images that contain the same particular object as the visual query, presented under a variety of modifications defined by textual queries. Its design and curation process keep the dataset compact to facilitate future research, while maintaining its challenge—comparable to retrieval among more than 40M random distractors—through a semi-automated selection of hard negatives. To overcome the challenge of obtaining clean, diverse, and suitable training data, we leverage pre-trained vision-and-language models (VLMs) in a training-free approach called BASIC. The method separately estimates query-image-to-image and query-text-to-image similarities, performing late fusion to upweight images that satisfy both queries, while down-weighting those that exhibit high similarity with only one of the two. Each individual similarity is further improved by a set of components that are simple and intuitive. BASIC sets a new state of the art on i-CIR but also on existing CIR datasets that follow a semantic-level class definition. Project page: https://vrg.fel.cvut.cz/icir/.

Instance-Level Composed Image Retrieval
Bill Psomas 1+
George Retsinas
Nikos Efthymiadis'
Panagiotis Filntisis Giorgos Tolias'
Yannis Avrithis
Petros Maragos
Ondrej Chum
VRG, FEE, Czech Technical University in Prague
"Robotics Institute, Athena Research Center
National Technical University of Athens
*Hellenic Robotics Center of Excellence
SIARAI
archival photo
during sunset
aer fal viexpoint
night, with full
as a scale model
image query
composed positives
visual hard negative
lextual hard negatives
composed hard negatives
Figure I: We introduce i-nstance-level Composed Image Retrieval (i-CIR) evaluation dataset. Given an image query depicting a specific instance (e.g., Temple of Poseidon) along with a modifying text query, the task is retrieve images showing the same instance altered according to the text (composed positives). Unlike existing datasets [54, 30, 21, i-CIR explicitly ensures the presence of challenging negative examples across three distinct dimensions: visual, textual, and composed.
Abstract
The progress of composed image retrieval (CIR), popular research direction in image retrieval, where a combined visual and textual query used, is held back by the absence of high-qualitg training and evaluation data. We introduce new evaluation dataset, i-CIR, which, unlike existing datasets, focuses on an instance- level class definition. The goal retrieve images that contain the same particular object as the visual query, presented under a variety of modifications defined by textual queries. Its design and curation process keep the dataset compact facilitate future research, while maintaining its challenge- challenge-comparaalee comparable to retrieval among more than 40M random distractors-througgh semi-automated selection of hard negatives. To overcome the challenge of obtaining clean, diverse, and suitable training data, we leverage pre-trained iision-and-language models (VLMs) in a training-free free approach called BASIC. The method separately estimates query image- to-image and query-text- sepery--exttto--oooe to-image similarities, performing late fusion to upweight images that satisfy both queries, while downweighting those that exhibit high similarity with only one of the two. Each individual similarity further improved by set of components that are simple and intuitive. BASIC sets new state of the art on i-CIR but also on existing CIR datasets that follow a semantic-level class definition. Project page: https://ver fel. cvut. cantciz/icir///
Equual contribution
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Introduction
Composed image retrieval (CIR) combines image-to-image retrieval and text-to-image retrieval. CIR uses composed query, iee. image and text, retrieve images whose content matches both the visual and textual parts of the query. Vision and language models (VLMs) [41, 25, 26, 20, 58] provide the foundation for developing CIR methods, either through further training [30, 11, 31] or in training-free manner [39, 9, 23]. The use of VLMS, owing to their large-scale pre-training, enables CIR to operate in an open-world setting and compare any kind of visual textual content. This capability paves the way for novel applications and advanced methods to explore and browse large image collections. However, the main limitation of CIR lies in the lack of appropriate data for both evaluating progress and training models. This work addresses these challenges.
Existing CIR datasets [30, 54, 2] often suffer from poor quality [2. 18] due to their construction process, Le. two similar images are selected automatically and their difference is textually described. This approach incorrectly assumes that such difference always forms meaningful text query for retrieval, regardless of the image pair. In contrast, given one image, we first specify a textual modification such that both together form a meaningful composed query. We then identify positive and large set of hard negative images to construct i-CIR, a compact yet challenging dataset. The goal retrieve images that depict the same object instance as the image query, under the modification described by the text query. Such an instance-level object class definition is missing from existing datasets and is identified as limitation by prior work [44]. By integrating diverse object types and modification types, i-CIR accurately reflects a wide range of real-world use cases.
CIR methods that rely further training on top of VLMs require large amounts of training triplets in the form of (query image, query text, positive image), which are challenging to obtain at scale. As result, training is typically performed or on automatically generated triplets obtained through crawling [60, 21, 29] or synthetic data generation [13]. However, these automated methods significantly compromise triplet quality, and in all cases, the diversity of visual object types and textual modifications remains limited compared to the variety present in VLM pre-training, thereby restricting generalization ability. Instead, we develop a training-free Baseline Approach for Surprisingly strong Composition, BASIC, which fully leverages existing VLM capabilities and remains adaptable future advances.
small sets of manually labeled triplets [51, 30, 1, 5],
BASIC separately computes the similarity with respect to each query component and performs fusion inspired by the classical Harris corner principle [15]; both responses must be jointly high, rather than just one of the two. The image-to togge-to-ing to-image dot product similarity is enhanced through a projection computed analytically not in the image space, but in the text representation space. This is facilitated by a large language model (LLM) that provides common object names and typical textual modifications. The aim is increase distinctiveness regarding object variations, i.e. to better represent image objects beyond other visual cues, while achieving invariance to textually described modifications, so that the same object is retrieved despite variations. Interestingly, this projection can also be equivalently applied solely on the query side, enabling user-specific or application-speciin customization. The text-to-image dot product similarity is refined via query-time contextualization process designed to bridge the distribution gap between the text inputs seen during VLM pre-training and the text queries used at inference. Our contributions are summarized as follows:
We introduce i-CIR, new evaluation dataset for CIR, meant to retrieve images containing the same particular object as the visual query under modifications defined by the text query.
We introduce BASIC, a training-fre approach leveraging pre-trained VLMs for class-level or instance-level CIR that based on image-to-image and text-to-image similarities, without the need to update the database embeddings.
BASIC new state of the art on i-CIR and across existing class-level CIR datasets.
2
Related work
Methods.
While early methods like TIRG [51], CIRPLANT [30] and CLIP4CIR [1] rely on
supervised training with annotated triplets, recent efforts in zero-shot CIR (ZS-CIR) avoid triplet supervision and fall into three main categories. Textual-inversine methods (e.g. Pie2Word [44], SEARLE [2], ISA [8], LinCIR [14]) map the reference image to a pscudo-text token, which is then

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composed with the modification text in the language domain and processed by a vision-language model. seeudo-triplet approaches (e.g. TransAgg [29], HyCIR [21], CompoDift [13], CoVR-2 [50]. MCL. [27]) generate synthetic training data using LLMs [28] and image generative models [42], either from caption-editing strategies or from natural web-based image pairs. Training-free methods (e.g. WeiCom [39], FreeDom [9], CI [23], GRB [47], WeiMoCIR [55]) leverage off-the-shelf VLMs [41, 25, 26] and LLMs [49] to perform CIR without any additional training by either recasting it as text-based retrieval or fusing visual and textual embeddings directly via weighted sums or geometric interpolations.
Datasets. Key benchmarks include Q [54] (77k images, 30k triplets across three fashion sub-tasks) and CIRR [30] (22k images, 37k triplets), both criticized for label ambiguity, high false- negative rates, and text-only shortcuts [2, 18], and recently refined by [18]. CIRCO [2] (Ik queries over 120k COCO-unlabeled distractors, 4.53 targets/query) extends this paradigm with more diverse negatives. Four additional domain-conversion datasets- Lmaasett-- [16] (30k stylized images of 200 classes four style domains), MiniDoominNNet [62] (140k images of 126 classes in four domains), NICO+ [61] (89k images of 60 categories in six contexts), and LTLL [10] (500 images of 25 locations)-explore class-level retrieval under style or context shifts. Concurrent to our work, ConCon- Chi [43] introduces an image-caption benchmark for personalized concept-context understanding, designed for text-to-image generation, retrieval, and editing. Their concepts correspond to our instances, while their contexts parallel the modifications expressed by our text queries.
3 i-CIR dataset
3.1 Overview and structure
We introduce the i-nstance-level Composed Image Retrieval (i-CIR) evaluation dataset. Following the instance-level class definition [40, 56], we group all visually indistinguishable objects, i.e. the same particular object, into a single class. For example, class may correspond to (i) concrete physical entity, such as the Temple of Poseidon, or (ii) a fictional yet visually distinctive character or object, such as Batman. In practice, if a human observer can confidently state that multiple visual manifestations represent the same object, they belong to the same instance-level class.
Given a composed query (q", q¹) consisting of a visual query q" depicting a particular object, also referred as object instance simply instance, and text query g¹ describing modification, the goal is to rank a database of images such that those depicting the same instance under the requested modification appear the top. We refer these images as composed positives or simply positives. For each composed query. we consider the following types of hard negative images: (i) visual hard negative: depicts an identical visually similar object as q" but does not match the textual modification q², (ii) textual hard negative: matches the semantics of q" but depicts a different instance, typically from the same semantic category, (iii) composed hard negative: nearly matches both query parts, while one of the two may be identically matched, i.e. depicts an object similar/identical to qº with semantics similar to q¹, or an object similar to q" with semantics identical/similar to n¹ Examples are shown in Figure and Figure 3.
All types of negatives, including non-hard ones, are treated equally during evaluation. However, we include a significant number of hard negatives in our dataset to create a challenging yet manageable benchmark that supports future research. There are n² image queries for the same instance combined with n' text queries that are combined to construct n n'nn' composed queries (values of n and n' vary per instance). Unlike typical retrieval benchmarks that use single common database for all queries, we employ the same image database for all n"n composed queries of an instance, but different database for queries of other instances. This design ensures scalable and error-free labeling, avoiding the impracticality of verifying each database image positive or negative for every query.
3.2 Collection and curation
The dataset construction process combines human input with automated image retrieval-. Our aim is to curate, for each instance, composed queries, sets of corresponding positives, and a sttu-sstde set of challenging hard negatives, with all i-CIR images sourced from the LAION [46] dataset.
"we perform image-to- image and text-to-ine text-to-image retrieval using dot product search based on image and text representations obtained from OpenAl CLIP [41].
3

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45
73
# composed queries
700
insttances
30
instances
insluncces
30
500
1.5
25
250
25
30
2
1,000
4,750
8,500
0
D0
100
image queries
text queries
negatives
positives
Figure 2: i-CIR statistics. From left to right: Number of (a) image queries, (b) text queries, and (c) hard negatives per instance; (d) positives per composed query.
The process for each instance begins by defining the object instance, egg. Temple of Poseidon, and selecting semantically meaningful modifications, e.g. "at sunset", while avoiding implausible ones, e.g. "with snow"' We then create seed images and seed sentences serve as queries for retrieving neighbors from LAION, which collectively form a candidate image pool that includes potential queries, positives, and hard negatives.
Seed images: to 5 high-quality images depicting (i) the object instance, e.g. the Temple of Poseidon, or (ii) composed positive, e.g. the Temple of Poseidon at sunset. These images gathered from web searches in Creative Commons repositories or personal photo collections. The neighbors retrieved from LAION are categorized as visual and composed hard negatives for cases (i) and (ii), respectively.
Textual descriptions of (i) the instance (e.g. "Temple of Poseidon"") (ii) another object of the same category (e.g. "Ancient Greek Temple""). (iii) rephrased versions of defined modifications (e.g. "a photo of dusk"") (iv) the instance under the modifications (e.g. 'Temple sunset"),, (v) an object of the same semantic category under the modification (e.g. "an Ancient Greek Temple at sunset"). The neighbors retrieved from LAION are classified as visual, textual, and composed hard negatives for cases (i & ii), (iii), and (iv & v), respectively.
Seed sentences:
of Poseidon
After building the candidate image pool, automated filtering removes low low-resolution, watermarked, or duplicate content using perceptual hashing and resolution checks. Annotators then manually inspect the remaining images to identify composed positives per composed query. Unmarked images are considered negatives. Visual hard negatives are associated with all composed queries of an instance, while textual and composed hard negatives are associated only with the specific composed query from which (or from whose text query) they were derived. Finally, annotators manually select images within the visual hard negatives to serve as image queries. All images that were neither filtered out from the candidate image pool nor selected queries form the database for this instance. Positives and hard negatives associated with a composed query are negatives for another composed query.
To avoid bias towards/against ELIP-based methods, seed images are discarded and not included in i-CIR, while seed sentences do not include the exact phrasing of text query.
3.3 Statistics and visualisations
Figure summarizes per-instance and per-query statistics in i-CIR. We include 202 object instances
and 750 K images in total. Each instance has 1-46 image queries (195 with > 1, median: 6) and 1-5 text modifications (median: 2), yielding 1.883 composed queries overall. Queries can be categorized either by their visual part (the object instance) or by their textual part (the modification). Each composed query has 1-127 positives (median: 5) and each instance's 's database contains 951-10,045 hard negatives (median: 3,420), creating challenging retrieval benchmark. Figure 3 illustrates set of randomly chosen pairings from the categorization. for each of eight visual-textual category combinations, we show the image query. composed positive, visual hard negative, textual hard negative, and composed hard negative. These visualisations highlight the rich diversity of i-CIR, both in terms of the wide array of visual categories (e.g. landmarks, products, fictional characters, tech gadgets) and the broad spectrum of textual modifications (e.g. viewpoints, attributes, contexts, additions), setting i-CIR apart from existing CIR datasets. More info in the Appendix subsection C.I.
3.4 Shortcomings of existing benchmarks
Commonly used CIR datasets include CIRR [30], FashionIQ [54], CIRCO [2], and ImageNet-R [16]. CIRR, FashionIQ, and CIRCO share a common limitation: their construction relies on an automated process to select two similar images, guided by either textual or visual similarity. These images form the image query and the positive pair. Due to the nature of the image sources, either there is no obvious relation of such selected pairs the relationship is typically at semantic level only,

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composeed positive
visual hard begative
textual
comapssed
composed postlive
visual negative negattve
composed negarive
queery
negative
hard negative
query
BRRRY
palstiing
visual: textmal: domain
fashion, textual: arcibute
sertal
(c) visual: landwark, tesual: vieupoint
extdear
(6) visua:: product, restual: appearance
vieual: fictionll, tesmual: projection
planet
visual: houschold, pontext
mobility textnal: atmibute
visual: textual: addition
Figure 3: Visualization of visual and textual category examples from i-CIR. For each of the eight randomly chosen category pairings (a-h), we display: the image query, the text query, the composed positive, the visual hard negative, the textual hard negative, and the composed hard negative.
rather than at instance level. Subsequently, a language-basee based description is generated to capture the difference between the two images. However, the lack of concrete differences between the images, often coupled with their low relevance, results descriptions that are either poor representations of meaningful text queries inadequate components of composed query. In many cases, the text query alone suffices to describe the positive image (Figure 6), making the image query redundant. Moreover, these datasets exhibit paucity of challenging negatives and substantial rate of false negatives in their ground-trutt annotations [2]. inflating reported performance. We present such cases in the Appendix subsection C.2.
Domain-conversion benchmarks such as ImageNet-R [16], NICO+ [61], and MiniDomainNet (MiniDN) [62] extend CIR to style context shifts (e.g.
but define posi- tives by semantic class membership rather than object identity, lacking instance-level granularity. LTLL [10] is the sole existing instance-level Nomain-conversioe dataset, but is extremely limited in scale (500 images of 25 locations, two domains) and provides only binary "archive" vs. "today" modifications. Furthermore, these benchmarks offer very narrow categorical confined to fashion items, L.TLL to a two-way temporal shift, and the domain sets to domain changes only. These semantic-level definitions, small scale, minimal textual variation, and weak negative mining in prior benchmarks motivate the creation of i-CIR.
is
4 A surprisingly strong baseline
In the task of composed image retrieval (CIR), we are given an image query g" E X and text query gf E X where X" is the image input space and X the text input space. The goal is retrieve images x" from a database X {aff.. .., x= and reflect the modifications specified by the text query. Features are extracted using pre-trained visual encoder Φ" X" + IR and text encoder Φ' X + Rdd, e.g. CLIP [41], which map image and text queries to shared embedding space of dimension Image-to-image and image-to-text similarities are computed via dot product of the corresponding features.
C X" that are visually relevant to the image query
The proposed training-free method, called BASIC, is based the assumption that both modalities in the composed retrieval query encode complementary information that jointly contribute to the retrieval objective. This makes the composed retrieval task analogous to performing logical conjunction over the two modalities: we seek results that are simultaneously relevant to both the image and the text 2
'hiiee this assumption holds well for standard composite tasks (e.g. this image and concept "winter"), it may not apply in tasks where one modality dominates (e.g. purely textual transformations) or where the text query is highly entangled with image content (e.g. CIRR-lik datasets). In such cases, the benefits of the proposed mechanisms may diminish.
5

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object corpus
stylistic corpus
image dataset X
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wuiling appl:
E>
Projection Matrix
Mona DNvE Uboenne
C
X
AND operation Harris criterion)
T
semantic projection
P
query
Image query
dot
contectuslizatio
product
10 an old archival
out laling toot quetp
Image
man bost QU0
something old archival
C+
Smin
Figure 4: Overview of our training-free composed image retrieval method BASIC. Given a query image and text, we apply centering and semantic projection, guided by corpora C+ and C-,, to suppress irrelevant dimensions. The text is contextualized using caption-like prompts. Both modalities are scored against the database with operation regularized by Harris-lik criterion to retrieve jointly relevant results.
normalization and fused via multiplicative "AND"
Following the aforementioned assumption. compute the similarity per modality and then combine them. We improve the representation per modality by removing modality-specifii noise and spurious correlations that interfere with their effective combination. In practice, visual features may be entangled with background clutter, image composition, or satasft-specific styles, whereas textual features can reflect lexical biases corpus-level drift. Figure 4 presents high-level overview of BASIC, which consists of a sequence of conceptually simple yet effective steps that progressively filter out the aforementioned noise and modality
artifacts from the image and text features.
Centering for bias removal. We remove modality-specific biases by subtracting mean features. These means typically capture low-level regularities unique to each modality: the average image feature reflects general visual patterns, while the average text feature captures common linguistic patterns. Subtracting them helps isolate semantic content from distributional bias. In particular, after extracting the image features q" (4(4") E I2 and text features q' (4(")) ( E R", also from the database images, we subtract precomputed image feature mean μ1" E R.d, and text feature mean je' E Rdd respectively. The centered features are
q" = q"=-"-p²" dd" n² and q'=q' - n² d
d'(g')
(1)
To ensure scalability and generalization, compute μº using large external dataset X", e.g. LAION [46]. Similarly, we calculate the text mean jz' on a predefined textual corpus which contains content-relevant concepts (see next step).
Projection onto semantic subspace. We aim transform the image features to retain information related to the main objects, while suppressing information related to image styles, object domains, or background setting, i.e. that correspond to text query modifications of common use cases. This is achieved by projecting into a construct this projection, we use two textual corpora: C',, object corpus containing object-oriented terms (e.g. "building", or "dog"), and C_, a stylistic corpus containing terms related to style, viewing conditions or contextual setting (e.g. "cartoon", "aerial view", or "in a cloudy day"). Inspired by [37], we compute weighted contrastive covariance matrix follows:
subspace derived from text CLIP features. To
C (1-1a))c_ -ac where C+
Σ (1) -') xEC+
(2)
C+
and a is an empirically determined hyperparameter. We extract the top-k cigenvectors of C e d to form a projection matrix P E 2 The cigenvectors capture directions with high variance in C.. and small in C_, emphasizing object-specific cues while suppressing style-related variation. We then project the centered image features x" = ((xx)) μ E Rd,, either query or database, as PT x E B Note that the corpora C+ and C_ need not match the domain of the retrieval database. Even generic corpora for C_,, egg. class names from ImageNet- 1K [6]. yield performance improvements, as the captured directions are semantically rich and broadly transferable.
Image query expansion. In the literature, image retrieval performance, recall in particular, has been shown to be significantly improved by query expansion [3, 12]. The proposed method benefits from

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applying the optional step of query expansion using the image query. Following [12], the original feature of the image query is enhanced by weighted combination of the top-ranked database features that it retrieves. The weights are an increasing function of the corresponding similarities.
Contextualization of text queries. CLIP is trained primarily on natural language captions and full sentences. As result, using single-word text queries (e.g. "sculpture") or sentence parts (e.g. "during sunset") constitute out-of-distribution input and may produce text features that are not well-aligned with image features. To address this, we introduce a contextualization step that enriches such textual queries with additional terms. Let q' be a raw text query (e.g. "sculpture"), We generate multiple caption-liks queries by combining g" with elements from the subject corpus C+. We add a random set of terms before (e.g. 'dog during the sunset")) and after (e.g. "sculpture dog') ") the text query. These composed phrases are embedded using CLIP's text encoder, centered, and averaged. This operation yields a more robust textual feature that better reflects how CLIIP interprets concepts in natural language (e.g. " "[something] during the sunset").
Score normalization and fusion. The final step combine similarities from the two modalities to rank the database items. Given the centered image query embedding q" E IR and the contextualized centered text query (either original, or expanded) embedding q' E Rd we compute similarities the centered embedding x E Rd of a database image x" E X as:
= (PTx', x", q"")"" and s' =
(3)
To reflect the complementary nature of the modalities, we fuse the two scores by multiplication: = s"s However, due to modality imbalance and differences in representation ranges, one modality can disproportionately dominate the final score.
Min-based normalization. To mitigate range imbalances, an affine re-scaling of the similarities in each modality is performed. The empirical minimum 8min < of the dot product in (3) is used, so that Smin is mapped to 0 and 0 is mapped to 1:
= (s - mmin (
We apply this both s" and st using predefined statistics for siin and siin estimated on an external dataset.
Fused similarity with Harris criterion. Finally, we fuse the normalized scores using multiplication and a regularizer inspired by the Harris corner detector. The final score is:
3"3 - X(3" + 5²)².
The first term rewards items that are jointly relevant to both modalities. The second term penal- izes items where only one modality is highly activated, thereby suppressing false positives from unbalanced queries. The scalar À controls the trade-off and is fixed across all experiments.
Computational complexity. The strengths of our approach stem from its simplicity and efficiency. The entire pipeline (deep-network) rraining-free and is composed of operations that scale linearly or sub- oub-linearly with the dataset size, since similarity computation over the dataset items is simple inner product and can be efficiently handled by existing libraries, e.g. FAISS [22]. The proposed similarity computation efficiently operates over a stored database of original CLIP represetttttas. The similarity s" is efficiently computed as follows
s"= "=<PTT (P" (x" 1²),, PTP
PT(p'' (q" p²)) = (x", "x"
(q" """"-"")-(( ("')))(""PPP((a - -1")).
where the first term is dot-produce computed over the unaltered database features and the second term is a query dependent constant. Thus, all computation related to centering and projection can be computed on-the-fly the query side. This is valuable, since the mean and the projection matrix can be alternated (e.g. with specific knowledge of the domain) without touching the stored index. This makes our method particularly well-suited for large-scale deployments, requiring no adaptation, no fine-tuning, and no backpropagation. See the Appendix D for more BASIC-related technical details.
Experiments
5.1 Experimental setup
Datasets and evaluation protocol. We evaluate BASIC on our proposed i-CIR as well as four composed image retrieval benchmarks: ImageNet-R, MiniDN, NICO++, and LTLL. Retrieval
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performance is measured using the standard mean Average Precision (mAP) metric. Average Precision (AP) computed per query by averaging the precision values at the ranks of all relevant items in the retrieval list. The mean Average Precision (mAP) is then obtained by averaging AP over all queries, providing a global measure of retrieval effectiveness that accounts for the order of relevant results. For i-CIR, we report the macro-mAP over instances, defined by first computing mAP per instance and then taking the mean of these per-instance mAPs across all instances.
Baselines and competitors. We include four simple baselines. "Text" scores each database image x" E X by (d'())), 60 (x"))) "Image" scores by (q"), &" (x"))) "Text + Image" combines the similarities by summation, "Text x Image" by product. We also benchmark BASIC against state-of- the-art zero-shot composed image retrieval methods: WeiCom [39], Pic2Word [44]. CompoDiff [13].
CIReVL [23]. SEARLE [2], MCL [27], MagicLens [60], CoVR-2 [50], and Free [9]. All
methods use CLIP with ViT-L/14 [7], whereas CompoDiff employs the larger CLIP ViT-G/14.
BASIC. For fair comparison, we also use CLIP [41] ViT-L/14 [7]. We set k: = 250 components for PCA, À = 0.1 for the Harris criterion and a = 0.2. These values were fixed once on small privately owned development set, named i-CIR dev- The corpora C+ and C-_ were automatically generated using ChatGPT [17]. The statistics Smin and s'nin were computed over a synthetically generated dataset constructed using Stable Diffusion [42] with automatically created prompts. More details are included the Appendix D and Appendix subsection E.I.
5.2 Experimental results
Per-category performance. In Figure we report the per-category performance of selected baselines and competitors on i-CIR split by the a) primary visual and (b) textual categories of the queries.
In Figure 5(a), BASIC ranks first in six of the eight visual categories, delivering particularly large margins on fictional (47.8% vs. 31.1% for SEARLE), mobility (45.8% vs. 29.3% for MagicLens), and technology (30.6% vs. 23.0% for Text x Image). It also leads product (33.7% vs. 26.7% for MagicLens), landmark (39.3% vs. 35.0% for MagicLens), and art (38.0% vs. 35.0% for Magicl.ens). The only exceptions are fashion, where MagicLenns edges 25.6% vs. 22.0% for BASIC, and household, where MagicLenns peaks at 29.1%, BASIC is second at 22.4%. 4%. In contrast, the other methods show uneven strengths.
Figure 5(b) further confirms the consistency of BA-
Table 1: Average mAP (%) comparison across datasets. t; without query expansion.
SIC. BASIC dominates projection (53.1% vs. 31.1%
for MagicLens), appearance (48.8% 48.8% vs. 36.8% for SEARLE), and domain (39.3% vs. 31.1% for Magi- cleens). It also leads on vewpoint (47.8% vs. 40.1% for MagicLens) and attribute (26.3% vs. 24.1% for MagicLens). BASIC is second on context (35.6% vs. 36.4% for MagicLens) and addition (24.0%
Method
ImageNet-R NICO+ MinDN LILL F-CIR
Text
0.74
1.09
0.57
5.72
3.01
Image
3.84
6.32
6.66
16.49
3.04
Text x Image
Image
6.21
9,30
9,33
17.86
8.20
7.83
9.79
9.86
23.16
17.48
WeiCom
10.47
10.54
8.52
26.60
18.03
28.2% for MagicLens).
Pic2Word
7.88
9.76
12.00
21.27
19.36
CompoDiff
12.88
10.32
22.95
21.61
9.63
Comparison with SOTA. We further evaluate the performance of BASIC against all considered base- lines and -fate-of-the-art CIR methods across five datasets, including i-CIR. Results are shown in Ta- ble 1. As observed, BASIC consistently outperforms all competing methods across the board. Runtime comparisons are provided in the Appendix subsec- tion E.
CIRcVL
18.11
17.80
26.20
32.60
18.66
SEARLE
14.04
15.13
21.78
25.46
19.90
MCL.
8.13
19.09
16.67
19.89
MagicLens
9.13
19.66
20.06
24.21
27.35
CoVR-2
11.52
24.93
27.76
24.68
28.50
FreeDom
29.91
26.10
37.27
33.24
17.24
FreeDom
25.81
23.24
32.14
30.82
15.76
BASIC
32.13
31.65
39.58
41.38
31.64
BASIC
27.54
28.90
33.75
38.22
34.35
Note. For (AU1 report mocro-wAl
5.3 Ablation studies
BASIC components. Table 2 presents a detailed ablation study on the contribution of each component of BASIC across all evaluated datasets. Starting with simple Textx Image baseline, we progressively add the components of BASIC, highlighting the cumulative benefits of each module.
Centering provides notable boost across most datasets (17.48% + 28.33% on i-CIR) with the exception of LTLLL, likely due to its narrow focus on landmarks. Normalization and Harris fusion further enhance retrieval, demonstrated by their removal, with min normalization being especially
8

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60
Text x Image
Pie2Word
SEARLE
FreeDom
MagicLens
mAP
40
20
product
tech
fictional
fashion
landmark
household
mobility
art
(a) Visual categories. mAP %) averaged over instances in same primary- visual category.
60
Text Imuge
Pic2Word
0| SEARLE
0
FreeDom
MagicLens
BASIC
50
MAp
40
20
10
projection
domain
attribute
appearance
addition
context
viewpoint
(b) Textual categories. mAP (%) averaged instances in the primary-level textual category.
Figure 5: Performance comparison i-CIR per primary category of queries. (a) Visual, (b) Textual.
Table Ablation study reporting average mAP (%) across datasets. Each row progressively adds or removes components of the proposed method: mean centering (Centering), min-based normalization
(Min Norm.), Harris criterion (Harris), text contextualization (Context.), semantic projection (Proj.). and query expansion (Q. Exp.). The first row (no component applied) corresponds to Text Image.
Centering
Min Norm. Harris
Context.
Proj.
Exp.
ImageNet-R
NICO+
MiniDN
LTLLL
1-CTR
X
X
7.66
9.26
9.48
19.78
17.48
X
X
X
12.16
9.95
12.16
16.93
28.33
X
X
X
12.06
17.20
17.72
22.20
27.30
x
X
15.06
17.79
29.70
28.42
18.61
21.01
33.74
/
/
27.54
28.90
35.75
38.22
34.35
/
X
17.31
13.96
21,22
22.42
31.78
X
V
26.18
30.61
33.64
34.50
25.85
/
30.75
29.82
38.85
40.65
31.61
24.50
22.74
29.65
19.36
30.75
32.13
31.65
39.58
41.38
31.64
critical, since its absence causes a significant drop. Harris consistently contributes moderate gains. Text contextaalization is also important. Its removal results in a substantial performance decline, particularly on datasets requiring nuanced language understanding (31.64% - 25.85% i-CIR). On image side, semantic projection accounts for the majority of the performance gain many cases, serving key enhancement. Query expansion offers additional improvements, particularly category-level datasets, though it leads to performance decrease i-CIR. Note that some components depend the presence of others to be effective (e.g. projection assumes centered features, Harris step requires min-normalized scores).
Controlling semantic projection. Table shows the effect of omitting C_, using generic negative corpus, using a dataset- specific corpus (generated via ChatGPT) de- signed to reflect the domain variability of ImageNet-R, NICO+ MiniDN, and LTLL.
Table 3: mAP(%) on each dataset using different neg- ative corpora. The first column lists the evaluation datasets.
Negative Corpora Source
Dataset
none
generic
Imagenet-R
NICO+
MiniDN
Imagenet-R R
30.15
32.13
33.22
30.74
31.17
30.91
Results indicate that leveraging application-
NICO+
30.67
31.65
30.84
31.17
31.20
related knowledge can improve performance,
MiniDN
38.64
39.38
39.34
38.75
39.58
38.68
LTL.I
41.80
41.24
41.33
43.39
42.09
43.98
particularly compared to omitting C'. This
CIR
31.51
31.64
31.61
31.20
31.32
31.06
idea further discussed in the Appendix sub-
section E.5.
i-CIR: Compact but hard. We use randomly selected images from LAION as negatives to assess how challenging i-CIR is in comparison
large-scale database that is commonly shared across all queries and lacks explicit hard negatives. Using performance of Text x Image baseline as reference (17. 17.48%) we find that more than 40M distractor images required for this baseline to

---

WeiCom
Text + Image
Text x Image
I0A
10
0
0.2
0.4
0.6
0.8
0
0.2
0.4
0.6
0.8
1
0.2
0.1
0.6
0.8
weight
weight À
weight
CIRCO CIRR FashionlQ
i-CIR
Figure 6: Modality bias via weight sweeps. mAP(%) vs. fusion weight À for three simple methods, where x=0 is text-only and x=1 is image-only. Compositional datasets should peak at interior À and exceed both endpoints. i-CIR shows strong interior optima and large gains over the best uni-modal baseline; CIRR and FashionIQ peak at X= indicating text dominance.
reach a similarly low performance. Note that the performance measured using unlabeled LAION images negatives is only lower bound because of the inevitable presence of false negatives. This is four orders magnitude larger than the 3.7K database images per query, on average, that i-CIR uses, or 1.5 orders of magnitude larger than the 750K database images used among all queries; the latter defines the experimental processing cost. More amalysis is provided in the Appendix subsection E.7.
i-CIR: Truly compositional. A dataset that requires composition should reward combining text and image, not either modality alone. To diagnose this, we sweep a mixing weight A [0. 1 between text-only (A=0) and image-only (A=1) similarity for three simple fusion methods (WeiCom, Text - Image, Text x Image), and plot mAP as a function of À (Figure 6). For each method we compute the composition gain A: the difference between the peak value of the curve and the best uni-modal endpoint, and then average A across the three methods. On i-CIR, the average composition gain is large: +14.9 mAP (+490% relative to the best uni-modal baseline), with peaks occurring at interior clear evidence that both modalities must work together. By contrast, shrinks to +3.0 mAP (+11%) on CIRR, +5.0 mAP (+26%) on FashionIQ, and +6,8 mAP (+ 167%) CIRCO. Moreover, the highest uni-modal performance of CIRR and FashionIQ is always when (A = 0), í.e. text-only. Thus legacy datasets reward composition only marginally, whereas i-CIR demands genuine cross-modal synergy; BASIC is designed for the latter scenario.
6 Conclusions
We introduced i-CIR, an instance-level benchmark for composed image retrieval with explicit hard negatives (visual, textual, and composed). It fills long-standing gap by providing an ambiguity- evaluation suite that rewards composition rather than single-modality shortcuts. We also presented BASIC, a simple, efficient, training-free method that compares favorably to both training- baasndg-based and training-free free baselines across benchmarks. BASIC built from few transparent components, whose combination delivers strong accuracy, transfers well, and exhibits broad hyperparameter plateaus. We hope i-CIR becomes a reliable target for assessing genuinely compositional retrieval, and that the simplicity of BASIC catalyzes adoption and further advances.
Acknowledgments. This work was supported by the Junior Star GACR GM 21 28830MMM the Czech National Recovery Plan Plan-CEDMO 2.0 NPO (MPO of Industry and Trade; the EU Horizon Europe programme MSCA PF RAVIOLI (No. 101205297) and HIERON-Hellenie Robotics Center of Excellence (No. 101 136568); the National Recovery and Resilience Plan "Greece 2.0" NNeeeteennraiinnUUUUUU project "Applied Research for Autonomous Robotic Systems" (MIS5200632); and Czech Technical University in Prague (SGS23/13//HK3////) and institutional Future Fund). We acknowledge VSB Technical University of Ostrava, IT4Innovatioss National Supercomputing Center, Czech Republic, for awarding this project (OPEN-33-6) access to the LUMI supercomputer, owned by the EuroHPC Joint Undertaking, hosted by CSC (Finland) and the LUMI consortium, through the Ministry of Education, Youth and Sports of the Czech Republic via the e-INFRA CZ project (ID: 90254). We also acknowledge the OP VVV project "Research Center for Informatics" (CZ.02...01/0.000.... 019/0000765).
21//2///111//20000 provided by the Ministry
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NeurIPS Paper Checklist
The checklist is designed to encourage best practices for responsible machine learning research, addressing issues of reproducibility, transparency, research ethics, and societal impact. Do not remove the checklist: The papers not including the checklist will be desk rejected. The checklist should follow the references and follow the (optional) supplemental material. The checklist does NOT count towards the page limit.
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1. Claims
Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Question:
Answer: [Yes]
Justification: The abstract and introduction accurately summarize our key contributions: the creation of an instance-level composed image retrieval dataset (i-CIR) with challenging hard negatives, the proposal of training-free CIR method (BASIC) that leverages existing VLMs, and the demonstration of state-of-the-art performance on i-CIR and multiple benchmarks. All claims are supported by detailed methodology and experiments in Sections 3-5, as also in supplementray material.
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is fine to include aspirational goals as motivation as long is clear that these goals not attained by the paper.
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2. Limitations
Question: Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification: We explicitly acknowledge in Sec. 3 (footnote) that our logical AND assump- tion may break down when one modality dominates (e.g.. pure style edits highly entangled image-tex, pairs like CIRCO). We point readers to additional method and dataset limitations in the supplementary material.
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The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
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While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't It acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [NA]
Justification: The paper contains formal theorems theoretical results requiring proof; is entirely empirical and algorithmic.
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
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Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
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Question: Does the paper fully disclose all the information needed to reproduce the main ex- perimental results the paper to the extent that affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?
Answer: [Yes]
Justification: We give exhaustive details of our dataset construction (including LAION search and retrieve, filtering and annotation protocols) and our method (all pre- pre-processing steps, corpora generation, syper-parameters k, A, a, normalization statistics), and we commit to relcasing both the dataset and code with scripts to reproduce every result.
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Justification: Both i-CIR dataset and code will be made publicly available through our project page https://vrg. fel. evut. cz/icir/.
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Justification: We use only publicly available, diconse-nnnotated sources (e.g.. LAION-derived URLs and rights-cleared repositories), and we respect licenses and site Terms of Service. All images i-CIR were reviewed by trained annotators; inappropriate, copyrighted, or privacy-sensitive content was removed. In categories where people are inherently present (e.g., apparel), we automatically pixelate faces and perform spot checks; no raw PII released. i-CIR is an evaluation-only benchmark, distributed under CC-BY-NC-SA with an explicit prohibition on surveillance/biometric or other privacy-invasive uses. We publish a misuse policy, provide "Report misuse/PII" channel, honor takedown requests. and reserve the right to revoke access for violations. Our method is training-free and does not scrape private data, minimizing environmental and privacy risks. No human-subjects research was conducted (IRB not applicable). Overall, collection, curation, release, and documentation follow the NeurIPS Code of Ethics.
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its original source license. For models/code, we use public VLMs and toolchains under their original licenses and cite them: CLIP (MIT). OpenCLIP (Apache-2.0), and, for anonymization, InsightFace (MIT). Our own release is evaluation-only under CC-BY-NC- SA and does not alter upstream terms; LICENSE files and attributions are included in our repo.
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Appendices
The Appendices includes the following information and results:
An ethics statement is presented in Appendix A.
Additional related work is presented in Appendix B.
The i-CIR structure, statistics and sample of image and text queries are presented in subsection C.I.
The shortcomings of some existing datasets (FashionlQ. CIRR, CIRCO) are discussed and supported by examples in subsection C.2.
Additional technical and implementation details of BASIC are discussed in Appendix D.
Additional results are presented in Appendix E. In particular:
The impact of srper-parameter values in subsection E.
In subsection E.2 show that the proposed components effective beyond the scope of composed image retrieval, i.e. for image-to-image and text-to-image retrieval too.
Detailed performance analysis per domain and category presented subsection E.3.
The performance comparison on instruction-like datasets is presented in subsection E.4. The impact of corpora, and how they control the semantic projection, is further explored subsection E.5.
Time comparison is presented in subsection E.6.
In subsection E.7 we present an experiment showing that i-CIR, despite being compact, as challenging as including more than 40M distractor images.
In subsection E.8 we present an experiment to demonstrate the i-CIR is not biased towards the model used in the data collection process.
In subsection E.9 we assess robustness to text-query wording by rewriting text queries, showing how phrasing alone markedly shift retrieval performance.
In subsection E. we present retrieval results in i-CIR, visually comparing different components of BASIC.
A Ethics statement
A.1 Broader-impact & dual-use discussion
Instance-level composed image retrieval offers clear societal benefits: it can power fine-grained search across museum and GLAM archives, helping curators and the general public surface rare artifacts and provenance links that would be impractical to discover manually; it can also bolster assistive-vision tools that describe surroundings to blind or low-vision users by retrieving the precise objects referenced natural- language queries. At same time, the very same instance-level match- ing capabilities entail dual-use risks: in the wrong hands they could enable large-scale surveillance, doxxing, or targeted advertising by tracing a specific building, vehicle, or logo across web-scale image corpora.
Precisely because of these concerns, dataset (i-CIR) intentionally designed to minimize such potential misuse:
Privacy-first curation. Annotators were trained to preferentially exclude images containing faces, license plates, private premises, or other personally identifiable information (PII) whenever doing so did not harm the task-e.g. e.g. for landmark queries where abundant alter- natives without PII existed. For categories where people are intrinsic the depiction (e.g. apparel modeled by humans), we retained the images but subsequently applied automatic, exhaustive anonymization: visible faces were pixelated across all retained images protect identity while preserving the visual evidence needed for instance- retrieval. Exam- ples of one image query per instance and unique text queries are presented in Figure C2 and Figure E9 respectively.
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Evaluation-only release. i-CIR is published solely as an evaluation benchmark, not training corpus. A model evaluated on i-CIR, containing no human-centric identifiers. cannot perform surveillance tasks without additional fine-tuning sensitive data. While such re-purposing possible in principle, requires access to an external, privacy-violating dataset; our license explicitly forbids this.
Domain specificity. Instance-level retrieval models are highly specialized to the visual domain on which they are trained. The gap between i-CIR (landmarks, products, fiction, fashion, tech gadgets) and human-centric surveillance domains further reduces direct trans- ferability. Moreover, the categories of instances represented in i-CIR landmarks, fictional characters, consumer products, fashion items, and technology gadgets (see Figure C1)-are are already common in benchmarks such as Google Landmarks (GLD) [53], INSTRE [52], Stanford Online Products [36], and In-Shop [33]; therefore, i-CIR does not introduce novel categories of high-risk.
A.2 Residual dual-use vectors & mitigations
Fine-tuning pathway - Risk: Techniques (e.g. architectures, loss functions) validated on i- CIR could later be fine-tuned on face-centric plate-centric corpus to build surveillance model. Mitigation: Our CC-BY-NC-SA license forbids biometric or privacy-invasive applications, and downstream works must inherit these restrictions. We additionally provide a misuse policy and reserve the right to revoke access for violators. We recognize, however, that determined adversary could ignore license terms. Our goal is to follow community best practice and exert every reasonable control that dataset creators can apply, while keeping the research benefits intact.
Dutaset-construction pathway - Risk: Our LAION-based search-and-retrieve scripts, if misused, could harvest a dataset rich in faces license plates. Mitigation: We release the scripts under the same restrictive license and with hard-coded filters that block sensitive keywords. The accompanying documentation explicitly instructs researchers not to re-purpose the pipeline for sensitive-content collection and encourages institutional review board (IRB) review for any modifications. Importantly, similar LAION-base search-and retrieee scripts have previously been employed [4]; our code does not introduce a novel capability but im-mleemennss such workflow with stricter safety defaults.
Object-of interest tracking Risk: Even without identifying people directly, one could try to track an individual via their belongings (e.g. rare handbag or customized laptop sticker). Mitigation: We explicitly prohibit using i-CIR to identify, profile, or infer the movements or associations of any person-directly or indirectly via personal effects. Retrieval results from i-CIR must not be construed as evidence of co-location or identity. We will conduct periodic red-team evaluations focused on object-level re-identification risks and update the release if failure modes are found. The project page will prominently include "Report misuse of the dataset" channel (web form/email) so the community can flag suspected abuse or PII leakage; reports will be reviewed promptly and may result in content takedown or revocation of access, consistent with our license.
Report misuse, i-CIR must not used to identify, profile, or infer movementt/assoociatonns any person. If you believe the dataset or code is being used in such a way or you discover content that reveals PII Pll-please submit report via the "Report misuse of the dataset" form or email us. We acknowledge reports promptly and may take actions including content removal or access revocation under the license.
A.3 Transparency & governance commitments
* License & API safeguards. CC-BY-NC-SA with explicit surveillance prohibition; research-only API gated behind user agreements; automated checks to block bulk reverse-image searches of sensitive facilities.
Community reporting. We provide clear channels for reporting misuse or ethical concerns (see "Report misuse" above). Substantiated reports will receive prompt acknowledgement and public response timeline, and may result in content removal or access revocation consistent with the license.
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A.4 Human-annotation protocol (labor conditions & oversight)
Employment & Compensation. All annotators are salaried employees of our institution (not nowddworkers)) Their contracts include full social-security coverage and wages that exceed 80% of a first-year PhD stipend, comfortably above the legally mandated minimum in our region.
Training & Oversight.
Domain training: A dedicated one-week training covered instance-level composed- image retrieval guidelines, annotation software, and quality-control protocols.
Ethics training: The same program included refresher modules on copyright compli- ance, privacy protection, and sensitive-content redaction.
Ongoing monitoring: Senior staff (including authors) conducted random spot-checks and weekly inter- wwwoaaato-agreemeeeee audits; annotators receive feedback whenever discrepancies arise, and, needed, re-labeling was performed.
B More related work
Datasets, Beyond composed image retrieval, instance-level retrieval has long served as a core benchmark for measuring fine-grained visual discrimination. Early datasets such UKB [34] and Holidays [19] introduced clean, small-scale benchmarks of object and landmark instances, while later landmark datasets like Oxford [40], Paris [40], and Google Landmarks [35, 53] expanded toward large-scale evaluation but at the cost of noisy or incomplete annotations. Domain-specific datasets followed, covering products [36, 59, 38J, fashion [32], and multi-domain mixed setups such as
INSTRE [52]. GPRI 200 [45], and UnED [57]. ILIAS [24] introduces large-scale, domain diverse, and error-free testbed with 1,000 object instances and manually verified query/positive images, evaluated against 100M YFCC [48] distractors; by restricting instances to objects emerging after 2014, mitigates false negatives without extra labeling and offers a challenging, unsaturated benchmark for both foundation models and classical retrieval.
C More on i-CIR and existing CIR datasets
C.1 Structure, statistics, visualisations, and limitations of i-CIR
Taxonomies. To better understand the diversity and structure of i-CIR, we organize all object instances into 3-level hierarchy of visual categories and all textual modifications into 1-level taxonomy of textual categories.
The visual taxonomy captures the type and nature of the object instance depicted in the image query. It begins with broad classes such landmark, fictional, product, tech, and mobility, which are further refined into subcategories and specific object types. For example, "Asterix" is categorized as fictional character + comic, while "Temple of Poseidon" falls under landmark -+ architecture + temple.
In parallel, each text query is annotated with one of seven high-level textual categories based on the nature of the transformation it describes:
Addition: One or more external elements are introduced into the scene alongside the instance such as people, objects, or animals. Examples: "with man proposing", "next coffee beans"'.
Appearance: A full transformation of the instance's physical form or structure. The object is still the main focus but appears in a completely different embodiment. Examples: "as figurine", *as sculpture", "as a scale model".
Attribute: A partial modification of the instance itself such as color, minor structural differences, or subtle material changes. The identity remains unchanged. Examples: "in purple color", "with yellow shoelaces"'.
Context: A modification in the surrounding environment scene, lighting conditions, or time of day. These affect the setting without altering the object instance itself. Examples: at night, during sunset, outdoors grass.
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The instance is rendered in different representational domain or medium, such as sketches, paintings, 3D renders, magazine ads, or comics. Examples: "as a painting". "in a manga panel".
Domain:
Projection:
The object instance is placed onto another object or surface, often as decora- tion or branding - such as clothing, packaging, or household items. Examples: "on t-shirt", "printed on pillow".
Viewpoint: A change in camera/viewer perspective, such aerial shots or top-down views. The instance and environment remain the same. Examples: "from an aerial viewpoint", "from top-dosm viewpoint".
These taxonomies, illustrated in Figure CI, provides insight into the types of visual and textual variation covered in i-CIR and supports performance breakdowns by category.
The 202 instances. Figure C2 provides a visual overview of the 202 object instances included in i-CIR. We intentionally set the total to 202-200 general instances plus 2 pet/animal instances (dogs)-included as a symbolic nod to the pets of our team while keeping the benchmark broad. Each image corresponds to distinct instance, ranging from iconic landmarks and branded products to fictional characters and vehicles. Roughly half of the instances are nameable (with a canonical, widely recognized name; e.g. "Eiffel Tower"), while the remaining half are everyday objects that are best described compositionally (e.g. "white-and-pin -ink dolphin plushie"). ) All main mainn results are reported on the full i-CIR; in ablations below we also report on two subsets: i-CIR named (only nameable instances) and i-CIR descriptive (only compositionally described instances).
Unique text queries. Figure E9 presents the complete list of unique textual queries used in i-CIR. Each query describes specific modification applied to an object instance, ranging from appearance and material to setting, domain, or interaction. These compositional cues form the basis of our retrieval queries and illustrate the rich semantic diversity captured in the benchmark.
Personally identifiable information redaction (face pixelation). To protect privacy while preserv- ing task utility, we automatically pixelated visible human faces across i-CIR. We used off-the-shelf detectors to localize faces and required overlap with a person detector before redaction, which reduces false positives from posters, figurines, or mannequins; detected boxes were conservatively expanded before applying a mosaic filter. For categories where people are incidental (e.g. landmarks), such images were preferentially filtered by annotators during curation; where people are intrinsic to the content (e.g. apparel), we retained the images but anonymized all faces exhaustively. Thresholds were set for high recall and audited via stratified spot checks; license plates and other PII were similarly removed or obfuscated when encountered.
Limitations of i-CIR Our semi-automatic pipeline trades some speed for fidelity: every composed query is manually vetted (e.g. for hard negatives, PII issues, etc.), so building i-CIR is slower and than fully automatic datasets, but yields an ambiguity-free benchmark the field currently lacks. Moreover, the design mostly assumes single salient object per image query (see Figure C2) and uses English-only phrasing with public-domain imagery. Despite filtering and automated redaction, some residual PII may persist; based stratified manual audits of random samples across categories, estimate the prevalence of unredacted faces or other PII to be < 2%. Our project page will include a "Report misuse / PII" channel, and we commit prompt review and content updates or takedown upon verified reports.
C.2 Shortcomings of existing benchmarks
CIRR. To substantiate our claims regarding the limitations of existing CIR benchmarks, we present qualitative retrieval results from the CIRR dataset using Text x Image in Figure E10. In "fewer paper towels per pack", semantically relevant match appears as the third result, yet it is incorrectly annotated as negative, showcasing clear false negative. In "the target is Pepsi bottle"', the query text alone suffices to retrieve relevant images containing Pepsi bottles: the image query adds minimal value beyond indicating the Pepsi brand. Notably, the fifth and eighth retrieved images include visible Pepsi bottles, but are labeled as negatives-likely due to the co-occurrence of other non-Pepsi items, further exposing annotation inconsistencies. In "two
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MARKINN
deco
faniioue
bandmatke
honehell
Mightng
1.CIR
unimali
pt
fetionall
gosting
chariic chariccer
Jevelly
pod fofumm
2002
250
(a) Visual categories organized in 3-1evel hicrarchy.
projection
domain
attribute
appearance
addition
context
(b) Textual modification calegories labeled at single level.
Figure C1: The i-CIR taxonomies showing (a) the 3-level hierarchy of visual categories and (b) the single-level taxonomy of textual modification categories. This taxonomy captures the diversity and distribution object instances and textual queries, and enables performance reporting per category.
antelopes standing one in front of the other", again, the textual query alone retrieves the intended concept, rendering the image query unnecessary composition. Yet, the seventh result clearly satisfies the query but is misclassified as negative. Lastly, in "widen the rows of TVs", supposed ground-truth positive contains four rows of TVs as the image query-while the sixth retrieved image includes five rows, arguably an even better match. Nevertheless, it is annotated negative, underlining the prevalence of false negatives and the lack of fine-grained visual reasoning the benchmark.
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Figure C2: The 202 instances of i-CIR dataset. We randomly sample query image per object instance.
FashionIQ. Figure E11 visualizes retrieval results on FashionIQ using Text Image, revealing several inconsistencies. In many cases, such as 'is solid black, is long" or "is green with four leaf clover, has no text", correct results appear within the top- but are annotated as negatives, showing false negative noise. In other cases, the query image plays minimal or redundant role; for instance, "is the same" easily retrieves the target based on visual matching alone, reducing the task simpler image-only similarity search. These observations support our
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claim that FashionIQ, like other CIR benchmarks, suffers from supervision that stems from retrofitting relations between pre-existing images. As result, the composed queries do not always require both modalities to be resolved.
CIRCO. Figure E12 presents retrieval examples from CIRCO using Text Image, which appears to have less flaws than CIRR and FashionIQ. While CIRCO exhibits fewer problematic cases, it is not entirely free of issues. Due to the class-level nature of the dataset, the distinction between positive and negative samples can sometimes be ambiguous- even for human annotators. For instance, in the case of the query "shows only one person", visually plausible matches like the third retrieved image clearly align with the query yet are labeled as negatives. Similarly, for the query "has more of them and is shot in greyscale", multiple retrieved images fulfill both criteria but remain unannotated as positives. These examples highlight the challenge of assigning unambiguous relevance labels at the class-level, even well-curated datasets.
D More on BASIC
On projection matrix P. As described in the main manuscript (Sec. 4), form the projection matrix P E Raak by using the top-k eigenvectors of C:
C = (1 - a.) C+ C_, where C+
(o'())-)')(
n²)
|C1 sEC+
These eigenvectors capture directions of high variance in C+ and low variance C_ when their corresponding eigenvalues positive. Conversely, eigenvectors associated with negative eigenvalues indicate directions of high variance in C.. and low variance in C.+, which we do not wish to retain.
To address this, we first count the number of "useful" components,
k+ = # (>x 0},
i.e. the number of eigenvalues of that are positive. We then set the final number of components as
k min k+).
In practice, for moderate values of the mixing parameter a, the initial choice of l (i.e. k = 250) typically already excludes negative eigenvalues. However, for large a (e.g. 0.8) negative eigenvalues can appear, necessitating this refinement.
Query expansion details. Given the initial projected query image embedding PT q" E Ed, we compute its top-k nearest neighbors { , C X in the database X. This set is augmented with the query image itself, avoid deviating from the original query. The centered features z = 6" (x²") - μ² E Rd are then aggregated into an expanded query q" E Rd via a soft attention- weighted mean:
k+1 wi
exp (3 (P 2 Z. PT qT)))
q"
where W.
(DI)
Σ exp (3 (PTTz. PT
The "temperature" hyper-parameter is fixed to 0.1. Again, following the computational complexity analysis of Sec. 4, we can avoid operating directly on the dataset features. In particular, the top-k retrieval can be accelerated (e.g. using FAISS [22]) by the formulation in the main manuscript, where sorting be done using only the term (x". PP (q )²))).
Contextualization insights. Contextualization provides a notable boost across all datasets (Ta- ble 2, main manuscript). However, the extent of this improvement varied by dataset. For example, contextualization does not produce the same gains for NICO+ as does for others. This dataset includes domains like "ock", "water"" and "outdoors". where combining them with objects (e.g. "rock dog" or "dog rock") does not result a particularly meaningful representation. In contrast, datasets like i-CIR benefit more clearly from context, e.g. "dog during sunset".
In some cases, adding the right level of textual detail, such as "a dog in a rocky environment", is important for forming well-defined text query. This can be crucial for further unlocking the
28

---

potential of BASIC. However, generating such enriched text requires the use of LLMs, which can introduce prohibitive overhead when applied on-the-fly for each query. Alternatively, a lightweight (shallow) network trained specifically to contextualize abstract queries could be used directly on their embedding space. While this promising direction, not explored this work.
Corpora creation. To create the various corpora used in this work, we used ChatGPT. We provided carefully designed prompt with few examples and generated batches of entries (e.g. 100 corpus entries per prompt). Typically, the created corpus needs a post-proccen prosceccosssing of removing duplicates and erroneous outputs. For example, there cases that after number of entries, ChatGPT returned noisy data, such as
Descriptionsl etc.
In particular, the prompt used to create the generic object corpus was:
Generic object corpus prompt
I would like you generate vocabulary of roughly 2, 5,000 visual classes that are similar in style and granularity to the categories found in ImageNet-1K. These should include:
Objects (e.g. "espresso maker", "screwdriver")
Animals (e.g. "Siberian husky", "hummingbird")
Scenes or natural elements (e.g. "iceberg", "volcano")
Everyday items (e.g. "shopping cart', "paper clip') Tools, instruments, clothing, vehicles, and so on.
The classes should cover broad range of everyday and recognizable visual categories, suitable for training or evaluating vision-language classification models. Some class names may be single-word (e.
"zebra") while others may be multi-word expressions (e.g. 'motor scooter", "garden hose").
Please do not include brand names highly specialized terms. Favor categories that have visually distinguishable appearances.
Output the vocabulary in chunks of 100 class names at a time, formatted as plain list (one per line). Wait for to say "next" before giving the next chunk.
In total, the generic corpora used consist of 1,800 entries in the object corpus and 1,000 stylistic elements in the negative corpus. To create the negative word corpus, which captures stylistic and contextual variations, we used the following prompt:
Negative word corpus prompt
We are working on composed image retrieval task, where the input consists of image and text. The goal is retrieve the most relevant matching image from a database. Typically, the image contains main object in particular "state"" This state to a style (e.g. retro, handwritten, futuristic) or contextual setting (e.g. next the sea, on the beach, beside table). For example: a retro mug, a cat next to table, a handwritten notebook. We would like you generate txx file containing 100 possible states in which an object can be found. Each line in the file should contain single state expressed natural language. Wait for me to say "next" before giving the next batch of states.
refer
The first outputs of the above prompt are presented in Figure D3.
Min normalization. To normalize and balance the two query modalities, we proposed min-based
normalization. To perform this step, the corresponding statistical values sy and s must be

---

wooden shelf
painted with graffiti
surounnedd candles
floating in vater
fireplace
hanging
wrapped in plastic
sitting on grase
under spotlight
Figure D3: Examples from the generic negative corpus. Entries are natural-language style and contextual setting modifiers.
precomputed. In other words, we empirically estimate lower bounds on maage-tt-imagee and text-to- image similarities after the centralization step. These values can be extracted from large existing dataset (e.g. LAION [46]). Instead, we opted to use a small synthetic dataset, generated via Stable Diffusion [42], where image is created directly from its text description.
There are two main reasons for this choice: 1) we obtain an accurate correspondence between each text caption and its generated image, and 2) we compute all pairwise similarity scores (which scale quadratically with dataset size), allowing us to efficiently recompute statistics under different settings (e.g. with or without applying the projection step). Performing this over millions of embeddings would be prohibitive, whereas the required statistics are conceptually simple values.
Specifically, we first used ChatGPT to generate 100 simple and diverse textual prompts (e.g. "aa dog', "a calm sea")) and 100 more complex ones (e.g. 'a hidden waterfall in lush, green jungle""). Each prompt was then used to generate 4 corresponding images using Stable Diffusion [42], encouraging intra-prompt diversity. All image-text and image-image pairs were then processed through our pipeline using the same CLIP backbone, and their similarity scores were computed. These scores were used to derive the normalization statistics snin and s min which are calculated after centralization.
For normalization, the minimum similarity values should always be negative. This is expected, the pipeline is built upon cosine similarity, and given sufficiently diverse set, centralization alone cannot shift all similarities into the positive range. This behavior is empirically validated in our experiments. Indicative values for the CLIP model are: s = -00777 and s -0.117.
Limitations of BASIC. BASIC assumes that both image and text components of query provide semantically distinct and complementary. equally important, information. This allows us to interpret similarity multiplication
logical conjunction operator. However, in tasks where one modality dominates (e.g. text-dominated edits as in FashionIQ [54] and CIRR [30]; examples shown in Figure E1l and Figure E10 respectively) content (e.g. CIRCO [2] text queries like "has a dog of different breed and shows jolly roger"), this assumption may not hold. In such cases, the benefits of our projection and fusion mechanism may diminish, or even have negative impact, compared to using only the textual modality.
where the text query is highly entangled with image
E
More experiments
E.1
Hyperparameter search of BASIC
During the development of BASIC, we set aside small portion the i-CIR crawl development set; none of these images appears in the final test benchmark. i-CIR dev consists of 15 object instances, 92 composed queries, and 45K images in total. All BASIC hyperparameters: contrastive scaling a, number of PCA components k, and Harris regularization X, were tuned once on this split and then frozen. To test the robustness of these choices, Table E1 runs a sanve-one-attset-on check: we treat each dataset in turn the development set (re-tuning a, k À that dataset only) and then evaluate the resulting configuration on all datasets. As expected, the best score per row typically occurs on the diagonal (in-domain), but the off-diagonal performance is very close, indicating that BASIC is not overly sensitive to where the hyperparameters are tuned. Notably, the configuration tuned on i-CIR cross-dataset dataset alternatives; i-CIR dev small convenience split used during development and not part of the public release.
dev performs on par with the cross
To further assess the sensitivity of BASIC to its hyperparameters, Figure E4 presents one-factor-at- a-time sweeps over the contrastive scaling a, the number of PCA components k and the Harris regularization weight X, holding the other two fixed. Across all datasets, the curves exhibit broad
30

---

Table E1: Cross-datass hyperparameter stability for BASIC. Each row lists the hyperparameters selected when tuning on the "Dev" dataset; each column reports the resulting score when evaluating that configuration on the "Test" dataset (higher better). Bold indicates in-domaín evaluation.
Dev + Test
i-CIR
ImageNet-R
NICO+
MiniDN
L.LII
Avg.
i-RR (ar = 0.2, k 300, == 0.05)
32.95
30.60
30.21
38.50
41.02
34,66
ImageNet-R (a 0.4, 200, 0.1)
30.67
32.63
31.39
38.49
40.41
34.72
NICO+ (o 0.2,
MiniDN = k 0.1)
30.95
32.55
31.85
39.81
41.32
35.30
=.1
32.55
31.85
39.81
41.32
35.30
LTLL 0.0, 300, A=
32,42
28.96
30.16
37.65
42.44
34.33
i-CIR des (n 0.2, 250. À 0.1)
31.64
32.13
31.65
39.58
41.38
35.28
plateau around the optimum: while the exact maximizer may shift slightly within the (coarse) grid, remains in the same neighborhood. We observe sharp maaabiiiteesmmdderas deviations from the tuned values incur only gradual changes in mAp- and the qualitative trends are consistent across datasets. Taken together, these results indicate that BASIC is robust to hyperparameter selection and that a single configuration transfers well across benchmarks.
25-
20.0
30.0
i7apelab-
Iragelec-i
15:
ICR
355
06 07
102 150 20n 253
150 A5C 500
002
025 030 alt 100 Li 130 115 200
contrustive 200 i2
Acorrponints
Hanbi sgMariuader watiiataooooooo 20
(a)
(b)
(c)
Figure E4: Sensitivity of BASIC to hyperparameters. mAp (%) on each dataset when varying one perparammete and holding the other two fixed: (a) contrastive scaling (b) number of PCA components and (c) Harris regularization weight A. Lines correspond to datasets the legend
(higher is better).
E.2 Unimodal evaluation of BASIC components
To better understand the contribution of each component in BASIC, we conduct a unimodal evaluation, by analyzing the system's 's performance when querying with only modality time. As shown in Table 2 of the main manuscript, the components of BASIC work in unison to produce the reported overall improvement. However, isolating the effects of each component unimodal setting can offer valuable insights into their individual contributions.
In this experiment, consider two separate retrieval tasks: retrieving the most relevant images given a text query, and retrieving the most relevant images given an image query. We choose ImageNet-R for this analysis, it allows for clean decomposition of the modalities. Table E2 presents the results, including a per-domain breakdown.
The following key observations can be made, supporting the analysis of the main manuscript:
Centering consistently improves performance for both text and image queries.
Contextualization significantly boosts performance the text modality, with an improvement of over 6% mAP, highlighting its importance enriching sparse abstract textual queries.
The semantic projection step offers the most substantial gain, increasing mAP by 17%, underscoring the importance of content-only preservation in the image modality.
Query expansion further enhances the effect of projection, adding an additional 3% improve- ment mAP, acting as complementary refinement mechanism. Notably, in the ORIGAMI case, this step reduces performance.
31

---

Table E2: Unimodal performance comparison in terms of mAP (%) using CLIP [41] for ImageNet-R, For each source domain (columns), we report the average mAP across all corresponding target domains. Each row progressively adds components in each modality, starting from the text-only or image-only baselines, respectively.
Modality
Method
CAR
SCU
TOY
AVG
Text
67.02
67.00
74.94
71.77
77.31
71.61
textual
+ centr.
72.12
72.52
79.62
76.10
79.53
75.98
context.
79.73
79.57
81.42
85.21
84.76
82.14
Image
28.62
19.77
47.46
29.40
33.80
31.81
visual
+ centr.
35.04
21.64
50.87
32.72
38.24
35.70
proj.
55.70
37.23
64.93
52.37
55.02
53.05
q. exp.
59.17
36.43
67.89
56.73
59.90
56.02
E.3 Detailed comparisons across domains and categories
To further understand how BASIC performs across different domains in the evaluated datasets, as well as across the visual and textual categories i-CIR, we provide detailed mAP results Table E3 and Table E4, using two different vision- Laiin-la models: CLIP [41] and SigLIP [58].
Table E3 presents detailed results across domains and categories using CLIP [41] as backbone. Several key observations emerge:
CLIP.
BASIC consistently outperforms all other approaches in the majority of domains and cate- gories.
FreeDom is the only method that achieves better performance than BASIC in any domain. Notably, this occurs only in the source domain PHOTO, and only within two datasets: ImageNet-R and MiniDN.
In ImageNet-R, the source domain ORIGAMI is particularly challenging for all methods. For BASIC, ablating the negative- term (a=0) drops mAP from 21.11% to 16.65%. whereas emphasizing (a=0.4) raises mAP to 23. 20%. This highlights how certain stylistic elements (e.g. "origami"") can become entangled with the underlying semantic content, and how the contrastive formulation of the corpora helps disentangle them.
On domain-conversion datasets, the ranking is stable: BASIC is consistently first and Freelom reliable second. On i-CIR, the runner-up is category-dependent Magicl is most often second and, in the household and fashion visual categories, it even ranks first first-likely reflecting its training distribution (consumer/e-commet) these fluctuations, BASIC remains the top method overall and the best performer across the majority of visual and textual categories.
imagery). Despite
SigLIP. Table E4 presents detailed results using SigLIP [58] as backbone. Not all methods in our evaluation compatible with the SigLIP backbone. Approaches such as MagicLens and Pic2Word rely on training lightweight models (e.g. MLPs or small Transformers) directly on top of CLIP features, making them inherently tied CLIP's representation space. Since SigLIP does not offer compatible pre-trained heads for these methods and retraining them on SigLIP features falls outside our scope, we restrict our comparison FreeDom -a training-frr -fee method that directly operates on image and text features from the backbone. The following observations can be made:
SigLIP improves overall retrieval performance across most datasets, with the only exception being NICO++, where drop in performance was reported.
BASIC significantly outperforms FreeDom on ImageNet-R, LTLL, and i-CIR. However, it reports lower mAP on both MiniDN and NICO++. Interestingly. in MiniDN, the two methods show complementary behavior- each outperforming the other on roughly half of the categories. This suggests that combining the strengths of both methods may further improve performance.
In i-CIR, there are categories where the Text x Image baseline is on par surpasses FreeDom. This highlights FreeDom's 's limitations adapting to fine-grained, instance-level retrieval.
32

---

Table E3: Performance comparison terms of mAP (%) using CLIP [41] across five datasets. (a-d): four domain conversion, where for each source domain (columns), we report the average mAP across all corresponding target domains; (e-f): i-CIR, where AP performance is averaged over queries grouped by their respective visual (e) or textual (f) category. AVG: average mAP over all source-target domain combinations. Bold: best; purple: second best.
Method
CAR
ORI
PHO
SCU
TOY
AVG
Method
PAINT
PHO
SKE
AVG
Text
0.75
0.66
0.60
0.80
0.75
0.71
Text
0.63
0.49
0.62
0.50
0.56
Image
3.89
3.53
1.02
6.03
5.82
4,06
Image
8.19
7.42
5.12
7:52
7.06
Text Image
5.89
5.00
2.66
9.23
9.18
6.39
Text + Image
9.74
10.71
8,20
9,94
Text x Image
7.22
5.72
7.49
8.65
9.19
7,66
Text x Image
9.72
7.21
15.55
5,44
9.48
Pic2Word
7.60
5.53
7.64
9.39
9.27
7.88
Pie2Word
13.39
8.63
17.96
8.03
12.00
CompoDDff
13.71
10.61
8.76
15.17
16.17
12.88
CompoDilf
19.06
24.27
23.41
25.05
22.95
WeiCom
10.07
7.61
10.06
11.26
13.38
10.47
WeiCom
7.52
7.04
15.13
4.40
8.52
SEARLE
18.11
9.02
9.94
17.26
15.83
14.04
SEARLE
18.72
23.75
21.78
MaggeLLnns
7.79
6.33
11:02
9.94
10:57
9.13
Magicl.ens
24.40
17.54
28. 59
9.71
20.06
FreeDom
35.97
27.97
37.21
29.91
FreeDom
41,96
31.65
41.12
34.36
37.27
BASIC
36.49
21.11
26.07
39.30
37.68
32.13
BASIC
46.44
34.84
38.70
38.34
39.58
(a) ImageNet-R
(b) MiniDomainNet
Method
AUT
DIM
GRA
OUT
Method
TODAY
ARCHIVE
AVG
Text
1.05
1.03
1.14
1.25
1.13
1.05
1.11
Text
5.09
5.30
5.20
Imagee
6.82
5.00
6.02
8.02
8.06
5:91
6.64
Image
9.06
23.60
16.33
Text Image
8.99
6.89
9.66
12.34
11.91
8.61
9.74
Text Image
10.17
24.47
17.32
Text Image
7.99
6,32
11.40
11.64
10.09
8.11
9.26
Text Image
15.88
23.67
19.78
Pic2Word
9.79
11:24
11.27
Pic2Word
17.86
24.67
21:27
CompoDiff
10.07
7,83
10.53
11.41
11.93
10.15
10.32
CompoDiff
15:45
27.76
21.61
WesCom
8.58
7.39
13.04
13.17
11.32
9.73
10:54
WeiCom
24.56
28.63
26.00
SEARLE
13.49
13.73
17.91
17.99
15.79
11.84
15.13
SEARLE
20.82
30.10
25.46
MagicLLnn
18.76
15:17
22.14
23.61
21.99
16.30
19:66
MagicLous
33.77
11.65
24.21
PreeDom
24.35
24.41
30.06
30.51
FreelDom
30.95
35.52
BASIC
31.07
34,62
36.9
32,18
24.24
40,00
(c) NICO+
(d) L.TLL.
Method
PBOJ
DOM
ATTR
APP
VIEW
ADD
CONT
Machul
Text
2,09
3.69
2.77
4.13
1.87
4.62
2.75
Text Image
Tex:
Text Inioee
21,05
2.55
4.79
1.05
27.88
12.85
3.91
3.41
25.50
8.15
1.5%
11.25
2.54
3.48
3.96
22.97
9.92
1.33
2.52
11.87
3.48
2.66
2.30
18.10
4.18
1.58
1.82
26.51
0.76
4.55
1.77
Text Image
Image
Image
0.90
4.08
2.46
0.63
10.07
4.37
26.16
3.28
1.13
14.
6.36
00.86
15.56
3.20
1.53
26.20
19.72
3.00
FlazWord
19:38
32.85
21.27
13:95
14.18
14.56
15:39
33.54
PicWWard
19.02
32.51
16.79
34.55
CompoDiff
10.51
15.00
19:21
3.56
6.18
7.41
4.54
13.04
CompoDiiff
3.72
13:49
6.85
15.78
8.53
2.88
16.34
WisCimn
18:15
5.63
18.67
54.27
WeiCom
20.55
20.55
13.75
21.88
37.00
9.51
34.56
SEARLE
31.10
11.59
17:25
22.75
SEARLE
27.00
16.20
17.20
36.
36.73
15.69
32.76
FreeDom
MasicLess
25.79
28.87
06
11:24
54,99
MagicLemm
FreeDom
CIReVL
27.55
21.46
23.66
17.86
16.42
25.
29.28
32.37
44.37
17 07.6
28.24
19.04
16.16
27.95
16.96
BASIC
47.85
30.34
45.79
22.42
30.99
21:99
33.67
38.04
BASIL
53.10
39:31
26.29
48.83
47.81
24.04
35.50
(c) i-CIR (visual)
(1) i-CIR (textual)
E,4 Additional composed image retrieval datasets
CIRR, FashionIQ, and CIRCO. Table E5 presents comparison between BASIC and prior work on CIRR, CIRCO, and FashionIQ. BASIC is outperformed by other approaches, such as CIReVL and Diff that do not perform well on i-CIR. The different datasets, ie.i-CIR, domain conversion datasets, and those in Table E5, have different objectives and reflect different tasks. As consequence, there is no single approach that is the best among all datascts. We argue that i-CIR better reflects real-world applications and use cases.
Unlike i-CIR and the domain conversion datasets, the text-only baseline consistently outperforms the image-only baseline. This aligns with our qualitative (Figure E10, Figure E11) and quantitative findings (Figure 6, main manuscript), confirming that in these datasets, the text query often provides sufficient information to resolve the task independently, rather than serving to refine or complement the image query. Notably, CIRR under the R@1 metric, the text-only baseline even surpasses CofpoDiff. Similarly, in FashionIQ, the average performance of the text-only baseline is only 2.65% R@10 below that of FreeDom. CIRCO appears to be less affected by this issue than the other two datascts. To further investigate the imbalance between modalities, conduct an ablation study by
33

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Table E4: Performance comparison in terms of mAP (%) using SigLIP [58] across five datasets. (a-d): four domain conversion, where for each source domain (columns), we report the average mAP across all corresponding target domains; (e-f): i-CIR, where AP performance is averaged over queries grouped by their respective visual (e) or textual (f) category. AVG: average mAP over all source-target
domain combinations. Bold: best; purple: second best.
Method
CAR
ORI
PHо
Scu
Toy
AVG
Method
CLIP
PAINT
Puo
SKE
AVG
Text
0.80
0.62
0.90
Text
Imuge
4.97
3.71
0.85
8.18
7.40
5.02
Image
5.07
7.53
3.68
6.15
5.61
Text Image
5.84
3.08
13.50
8.60
Text Image
7.79
11.33
10.80
9.02
9.74
Text Image
6.57
4.34
4.89
6.46
7.46
5,94
Text Image
3.00
2,60
4.34
3,18
3,28
FreeDom
49.46
38.11
47.52
41.82
PreeDom
57.14
43.47
59.71
52.21
53.63
BASIC
53.67
39.54
36.40
51.39
53661
46.92
BASIC
59.76
45.58
54.67
47.73 51.94
(a) ImageNet-R
(b) MiniDN
Method
AUT
DIM
GRA
OUT
RoC
WAT
AvG
Method
TODAY
ARCHIVE
AVG
Text
1.08
1,26
1.10
1,11
1.12
5.02
3.84
4.43
Image
6.19
5.19
5.42
7.67
7.44
5.62
6.25
Image
28.14
10.25
19.20
Text + Image
8.35
7.19
8.08
11.42
10.57
8.12
8.95
Text + Image
26.73
10.16
18.44
Text Image
2.31
2.91
3.26
3.53
3.25
2.90
3.03
Text Imaaee
3.49
4.87
4.18
FreeDom
30.28
29.96
33.86
37.16
31.1
FreeDom
BASIC
28.1
32.39
31.13
34.07
29.26
23,12
29.68
BASIC
45.01
39.09
42.05
(c) NICO+
(d) LTLL
Mechod
LAND
5408
310USE
1ECH
FASH
7800
ARET
Method
PROI
170M
AFIR
AFP
VIEW
ADD
CUNI
Text
Text
5.95
5.34
5.22
6.88
1.74
10.45
4.55
Image
1.31
1.52
Image
1.24
1.09
6.31
1.35
8.98
4.09
4.63
Image
Test Image
3.29
12.66
20.12
Imnge
Image
26.47
11.15
26.24
25.13
ProdDom
27.25
27.00
43.00
00.77
53.11
FreeDom
22.94
25.25
20.03
31.52
38.3 34
39.17
25.59
BASIC
50.65
53.45
39.56
88.87
58.84
52.83
45.11
61.43
BASIC:
53.41
51.17
42.19
50.62
63.73
47.42
50.90
(e) i-CIR (visual)
(f) i-CIR (textual)
removing the Harris regularization (denoted as BASIC *). Results show improved performance in CIRR and FashionlQ, supporting the hypothesis of modality imbalance.
Refined CIRR and FashionIQ. We evaluate BASIC and BASIC without Harris regularization (BASIC ") on the newly released CoLLM [18] refined [18] versions CIRR and FashionIQ Ta- ble E6. Both variants gain notably on the refined splits (e.g. up to +33.7 R@10 refined-FashionQQ SHIRT). These gains are consistent with CoLLM's goal reducing annotation ambiguity. This is
orthogonal to modality dominance (as evidenced by BASIC outperforming BASIC text-dominant settings) and to CLIP's phrasing sensitivities (e.g. instructional/rclatinnnl text).
E.5 Dedicated vs generic corpora
We further explore the impact of using dataset or domain-specific (dedicated) vs. generic corpora on retrieval performance. This analysis is conducted LTLLL, which contains a narrow range of object cioegories-ssecifficcl landmarks such as monuments and architectural sites.
The goal is to assess whether adding more targeted corpora can improve performance. While incorporating landmark-only corpus into i-CIR would be problematic, due to the large number of irrelevant distractors that can introduce noise when projecting into restricted semantic space, LTLL is composed entirely of landmark-relater imagery, making well-suited for such decomposition.
We experiment with two types of corpora:
Generic corpora, which include general stylistic descriptors.
Dedicated corpora, designed specifically for this task:
34

---

Table E5: Performance comparison using CLIP [41] across three generic composed image retrieval datasets. BASIC evaluated for the default settings, as well as without the Harris regularization (+), which found to be beneficial for these datasets.
Method
R@
R@5
R 10
R
Method
mAP@5
mAP@
mAP@:50 mAP@22
Text
3.76
Image
7.42
23.61
34.07
57.40
Image
1.50
2,02
2.76
3.13
Text Image
1241
36.15
49.18
78.27
Text + Image
4,06
5.20
6.29
6.85
Text Image
22.55
62.84
Image
11.64
14.28
Pic2Ward
23,90
51.70
$7.80
Pic2Word
8,70
9.50
10.70
11.30
SEARLE
24.20
$2.50
66.30
88.80
SEARLE
11.70
12.70
14.30
15.10
CompolDiff
18.20
53,10
70.80
90.30
CompoDill
12.60
13.40
15,80
16.40
FreeDoom
FroeDom
CIReVL
21.60
52.30
61.90
86.30
CIR&VL
18.60
19:00
20.90
21.80
MagicLens
30.10
61.70
74.40
92.60
MagicLens
29.60
30,80
33.40
34.40
53.90
15.95
16.77
18.19
18.94
58.80
15.98
19.00
CTRR
(h) CIRCO
Dress
Shirt
Toptee
Average
Method
R@ 10
R@50
R@ 10
@ 50
R@ 10
R@50
R@ 10
R@50
Text
14.53
32.92
20.51
34.69
21.83
39.57
35.73
Image
4.36
12.84
10.55
19.97
8.21
16.37
7.71
16.39
Text Image
17.40
35.60
21.84
36.26
23.30
39.37
37.08
Image
21.52
41.00
27.63
42.05
28.71
47.22
25.95
43.42
Pic2Word
20.00
40.20
26.20
43.60
27.90
47.40
24.70
43.70
SEARLE
20.50
43.10
26.90
45.60
29.30
50.00
25.60
46.20
CompoDiff'
32,20
46.30
37.70
49.10
38.10
50.60
36.00
48 60
FreeDom
16.80
36.30
23.50
38.50
24.70
43.70
21.60
39.50
CIReVL
24.80
44.80
29.50
47.40
31,40
53.70
28.60
48.66 (8.60)
Magicl ens
25.50
46.10
32.70
53.80
34.00
57.70
30.70
$2,50
BASIC
18.59
37.68
26.30
44.80
23.92
40.95
22.94
41,14
BASIC 19.98
39.86
29.88
47.06
26.21
44.57
25.36
43.83
(c) FASHIONIQ
Table E6: Performance on CoLLLM-refine CIRR and FashionlQ. Both BASIC and BASIC improve on the refined splits, indicating reduced annotation ambiguity.
Dataset
Split
Method
mAP
R@1
R@5
R@ 10
R@ 50
Legacy Refined
BASIC
24.34
15.83
40.89
53.90
82.27
BASIC
28.28
21.87
48.96
60.82
86.66
CIRR
Legacy
BASIC
26.89
17.98
44.92
58.80
86.51
Refined
BASIC
31.89
25.30
53.49
65.94
90.15
Legacy
BASIC
6.96
18.59
37.68
FashionlQ DRESS
Refined
BASIC
9.91
24.40
47.63
Legacy
BASIC
7.43
19.98
39.86
Refined
BASIC
11.27
28.59
52.43
Legacy
BASIC
10.93
26.30
44,80
Refined
BASIC
17.15
36.82
56.09
FashionIQ SHIRT
Legacy
BASIC
12.93
29.88
47.06
Refined
BASIC
22.55
43.54
62.40
Legacy
BASIC
10.28
23.92
40.95
Refined
BASIC
14.57
32.08
51.41
FashionIQ TOPTEE
Legacy
BASIC
12.08
26.21
44.57
Refined
BASIC
19.08
38.27
57.98
Arrhitectural terms (e.g. "Byzantine basilica",, "Crusader fortress", "slate tile", "oxi-
-
dized copper", "weathered limestone") -Tepporal/stylistic cues (e.g. "flash photography", "shadows of age", "digital camera photo", "restored image")
-
35

---

Table E7: LTLL dataset mAP (%) results using different combinations of positive (+) and negative (-) text corpora: generic (Gen) or dedicated (Ded). We consider different text queries with the same semantic meaning. Rows correspond to target domains which are the text queries used.
Target Domain
Only Gen+
Gen+ Gen-
Only Ded+
Ded+ Ded-
Gen+ & Ded-
Ded+ & Gen-
archive
41.41
40.00
42.83
43.00
43,60
40.97
today
42.13
42.76
43,96
45.97
44.38
45.74
mean
41.77
41.38
43.40
44.48
43.99
43.36
old
50.52
48.86
50.78
50.77
50.58
48.89
Bew
38.24
40.00
41.77
45.68
41.12
44.66
mean
44.38
44.43
46.28
48.22
45.85
46.78
vintage
54,04
53.92
54,30
57.37
57.43
53.86
today
42.13
42.76
43.96
45.97
44.38
45.74
mean
48.08
48.34
49.13
51.67
50.90
49.80
We also evaluate the effect of varying textual queries that convey the same underlying meaning, such as synonyms or stylistic variants. Examples include:
"archive" vs. "old" ""vintaee" - all describing temporally distant or aged imagery.
"today" vs. "new"" - both indicating contemporary or modern representations.
Table E7 presents these performance comparisons over different combinations of corpora and text queries. The following observations can be made:
Different textual queries, despite their close-almost identical-semantic meaning, can have substantial impact on retrieval results. The results for the first two textual pairs ("today" "now" and "archive" vs. "old"") reveal interesting trends. The query "today" is more effective at retrieving contemporary photos compared to "'now". In contrast, the "archive" underperforms relative "old'', Semantically, this is expected- "arccted""rrchivee" is not commonly used to describe old photos in natural language queries. To address this, we also include the query "vintage", which offers a clearer and more intuitive descriptor for aged or historical imagery.
The of dedicated corpora consistently improves performance across all textual variants. Even a single dedicated corpus yields a notable boost.
Adding negative corpus generally enhances performance, with the exception of the ("today", "archive"") pair when combined with the generic stylistic corpus.
The baseline mAP for the ("today"' "aachhive") setting is 41.38% using generic corpora. With dedicated corpora and well-matched textual queries the performance increases to 51.67 51.67%, representing gain of more than 10% mAP.
We further examine the case of i-CIR by leveraging its predefined visual categories, as illustrated in Figure Cl. Based on this taxonomy, we construct a dedicated object corpus of 300 elements tailored specifically to i-CIR. This setup serves proof-of-concept, it assumes access to detailed visual categorization-ine inniiiiatiionniifommattiiiio that typically unavailable in realistic retrieval scenarios.
To avoid introducing bias toward any specific category, which may collapse the semantic representa- tion towards specific direction, we ensure that objects are selected from across visual categories. As in previous experiments, we concatenate the dedicated corpus entries with those from the generic corpus.
Table E8: i-CIR performance comparison of different object corpora terms of mAP (%). CLIP [41] backbone is used. We compare the case of the generic corpus vs the dedicated corpus across the different visual categories of i-CIR.
Positive Corpus
FICT
LAND
MOB
HOUSE
TECH
FASHI
PROD
ART
Generic
47.85
39.34
45.79
22.42
30.59
21.99
33.67
38.04 37.88
Dedicated
49.48
39.35
47.05
21.92
34.45
22.91
35.00
36

---

40
30
MAP (%)
20
BASIC
BASIC- no expansion
contextualization
Freedom
10
WeiCom
Pic2Word
SEARLE-XL
0.04
0.06
0.08
single query in seconds
Figure ES: Latency vs. mAP(%) on ImageNet-R, Comparison methods from the literature and BASIC with and without query expansion and contextualization.
Table ES reports per-category mAP i-CIR. The category-aware dedicated corpus yields improve- ments in six of eight categories (parity LANDMARK, minor drops in HOUSEHOLLD and ART). Note that these corpora were automatically generated with ChatGPT from short prompt (see example in box) and were not band-balanced match i-CIR taxonomy; their distribution only loosely aligns with the actual category frequencies. Despite this mismatch- and the fact that simply concatenate the dedicated with the generic corpus we still observe consistent gains, suggesting that a weak, category- aware prior can help without tightly fitting the dataset. We view this as proof-of-coneeee
Dedicated word corpus prompt
Provide txt file with 300 distinct household objects (as they are labels for classification task). Be broad and diverse.
E.6 Time requirements
Figure E5 presents a per-query latency comparison between BASIC and several existing methods from the literature: WeiCom, Pic2Word, SEARLE-XL, and Freedom, evaluated on ImageNet-R. BASIC achieves the highest performance, reaching mAP of 32.13 with latency of 70.96ms per query. When query expansion is disabled, the performance drops by 4.59 mAP, while the latency improves slightly by 0.48ms. The primary overhead in BASIC is the contextualization step. When removed, BASIC (33.23ms) becomes faster than Freedom (34.6) 344.66m), SEARLE-XL (36.09ms), and Pic2Word (33.50ms), and only marginally slower than WeiCom by 0.03ms. Note that no further retrieval optimization has been applied for these experiments (e.g. FAISS [22]).
We intentionally exclude CiffoDiff [13] and CIReVL [23] from this latency comparison: CompoDiff relies on diffusion generator at inference, and CIRe VL invokes a captioner (BLIP 2 [25]) and an LLM (LLaMA [49]). These pipelines are orders of magnitude heavier and are not designed for low-latency retrieval, so including them would make the plot incomparable and obscure the efficiency trade-offs among lightweight methods.
As expected, aside from the contextualization step which introduces the overhead of computing additional CLIP embeddings (fixed to generating 100 such descriptions across all experiments) the proposed approach remains very lightweight. The overhead introduced by contextualization is current limitation of the method, but could be addressed by integrating a separate shallow network that contextualizes the CLIP embedding of given text query, as discussed in Appendix D.
E.7 i-CIR: Compact but hard
We use randomly selected images from LAION as negatives to assess how challenging i-CIR is in comparison to large-scale database that is commonly shared across all queries and lacks explicit hard negatives. Figure E6 shows that the performance drop on i-CIR is comparable to the degradation
37

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Text x Image on L-CIR
Text x Image on rand. 2-CTR
MAP (8)
30
10
5
10
15
20
25
30
35
40
45
50
# of distractors (M)
Figure E6: Impact of curated vs. random negatives on retrieval difficulty. We plot mAP on i-CIR with its 750K curated hard negatives against the same benchmark augmented with up to 50M random LAION distractors. Even tens of millions of distractors do not match the difficulty imposed by curated hard negatives.
caused by tens of millions of randomly sampled LAION images. Using the Text Image baseline, show that i-CIR, with its curated hard negatives, substantially more challenging than a 400-image LAION distractor set.
E.8 Collection bias
While we take explicit steps to mitigate potential bias toward CLIP ViT-L/14-based methods during data collection such discarding all seed images and ensuring that seed sentences do not exactly match any text query, the possibility of residual bias cannot be entirely ruled out. Since the retrieval of candidate negatives relies on CLIP ViT-L/14, one might expect this model to underperform relative to weaker models due to the increased difficulty of the curated negatives tailored to its embedding space. To this, we evaluate the standard Text Image baseline using weaker model, CLIP ViT-B/32. The results show that ViT-B/32 achieves 18.66% mAP on i-CIR, notably lower than the 23.28% mAP ViT-I/14. This confirms that no bias favoring alternative models has been introduced and that the performance of ViT-L/14 is not artificially deflated by the collection process.
E.9 Effect of text query style retrieval
text query can be almost equivalently formulated different ways, e.g. concisely ("engraving"") in longer sentence ('as vintage stylish engraving"), in relational way ("the same landmark as the one depicted in the image but as an engraving'), and in an in- structional way (''engrave the landmark depicted in the image"). In the context of i-CIR, we refer to those cases as original, longer, relational, and instructional, respectively. The queries of i-CIR are brief and comprehensive, while existing datasets like CIRR and CIRCO often relational or instructional, while also having larger lengths.
To probe the robustness of BASIC against such text query styles, we automatically rewrite (via ChatGPT) all text queries i-CIR named: and we present the results in Table E9 (A is the relative change with respect to the original row).
Table E9: Impact of text query style on retrieval. We compare the original (concise) queries from j-CIR named with automatically rewritten longer, relational, and instructional variants and report mAP for BASIC, FreeDom, and MagicLens. A is the relative change vs. the original row.
Text-query Variant
BASIC mAP
BASIC A%
FreeDom mAP
FreeDom 4%
Magicl mAP
Magicl.nns A%
Original (concisc)
47.39
25.14
32.00
Longer
45.02
23.91
-5
26.90
16
Relational
38,95
18.99
29.34
Instructional
37.47
-21
18.31
227
28.85
10
38

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Longer. We expand all text queries by adding 1 to 4 extra words while preserving their meaning. On average the phrases grow from 4.4 to 7.9 words, an increase 3.5 words 79%, effectively almost doubling their length. The median rises from 4 to 8 words, the shortest query goes from 1 to 4 words, and the longest from 13 15. Example: "crowded" becomes "in a dense and crowded scene". The ChatGPT prompt used to convert text queries is presented below.
Longer text query prompt
Take each text query and, without altering its core meaning introducing new concepts, enrich it with tasteful descriptive words clarifying phrases that its length is roughly doubled. Preserve the original order and casing and return each pair as "original query": "augmented longer query" with no additional commentary. Example: "crouded" "in a dense and crowded scene"
We observe that BASIC has small performance drop similar to that of FreeDom, while MagicLens has larger drop, therefore revealing a new advantage of training-free methods (relying on CLIP). Related to the above question, MagicLens presumably has larger drop due to shorter sentences in its training.
Relational. To give every text query explicit link to its paired reference image, rewrite all text queries by prepending a short relational clause while keeping the original order and cas- ing intact. Example: 'with fog" becomes "the same object instance depicted in the image but with fog". This yields suite of explicitly relational captions that stress compositional understanding without altering the original intent. The ChatGPT prompt used convert text queries is presented below.
Relational text query prompt
Rewrite every text query so that it explicitly refers to the same object instance of the image query, while preserving original words in the same order. Simply prepend a concise relational clause, avoid introducing new concepts or proper names, and return each pair as "original query":: "relational query", with no additional commentary. Example: "with fog" becomes "the same object instance depicted in the image but with fog"
We observe that the drop of BASIC is noticeably smaller than that of FreeDom, but also greater than that of MagicLens. This pronounces the benefit of training for composed image retrieval, and drawback of training-free methods, which is due the fact that there information during the CLIP pre-training. Nevertheless, BASIC still performs reasonably well.
little relational
Instructional. We recast every text query as an imperative instruction that tells the system what to do with the reference-image object. Concretely, we prepend action verb (e.g. "turn", "place"', "render"") and then clause like "the object depicted in the image", then append the original words in the original order. Example: "in live action scene" becomes "adapt the object depicted in the image into live action scene". The ChatGPT prompt used to convert text queries is presented below.
Instructional text query prompt
Rewrite each text query concise imperative instruction that starts with an appropriate action verb, explicitly mentions something like "the object depicted in the image", preserves the original words in the same order, introduces no new concepts, and output each pair exactly as "original query": "instructional query" with no commentary.
The performance pattern across the methods in this case mirrors the relational case.
39

---

Additionally, we perform the reverse mapping for 10 manually selected relational queries of CIRR that are converted into shorter and absolute descriptions. For example, "Make the dog older and have two birds next to him and make everything look like painting" becomes "as painting of an older dog with two birds" and "Change the background to trees and remove all but one dog sitting in grass looking right" becomes "as a single dog sitting in grass with trees in the background".
Interestingly, evaluating using the 10 rewritten queries of CIRR (shown in Table E10) to make them non-relational (concise) demonstrates a large performance increase for BASIC from 18% mAP (10% R@ 1) to 44.4% mAP (40% R@ 1). This signifies that the relational description might not be the best way describe those queries either.
Table E10: Making relational CIRR queries concise. Converting 10 relational CIRR queries into concise, non-relational descriptions substantially improves BASIC, shown via mAP and recall.
Text-query Variant
mAP
R@1
R@5
R 10
R@
Relational
18.0
10.0
40.0
60.0
80.0
Concise
44.4
40.0
60.0
70.0
90.0
E.10 Retrieval visualizations across BASIC components
Figure E7 and Figure E8 qualitatively demonstrate the effects of centering and the performance of the full BASIC compared the Text Image baseline on i-CIR. Notably, even centering alone yields substantial performance gains.
40

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Text Image
image
top
tup
MCQUEEN
Text x Image + centering
image query
Voaan
MATTAro
BASIC
guery
Text Image
imaae guery
top
top
top
tоp
t0p
10p
top
top
ooler
Text Image centering
image
to
top
top
kp
top
top
kmp
(op
kop
Top
query
1e. steat
BASIC
image query
top
top 2
top
top
top
top
tap
top
tap
top
3
5
10
ddaat
Figure E7: Retrieval results for Text x Image, Text Image with centering, and BASIC on i-CIR.
41

---

Text Image
image
top
tup
Text x Image + centering
image query
centell steppsies
BASIC
guery
centel
Text Image
imtage query
top
top
top
top
t0p
10p
top
tap
top
Text Image centering
image
top
top
top
top
top
krp
Corp
txp
Top
query
BASIC
image query
top
top 2
top
top
top
top
tap
top
tap
top
10
Figure E8: Retrieval results for Text x Image, Text Image with centering, and BASIC on i-CIR.
42

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'along cetrie gA3A
generated digital
havinng
"placed tray
placcdd a plastic
rarranged bexide
figure
hald
beach
balloon
architaciural plan
hald
holder
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holding bunch bananaa
"plugged top of clothea gloving Hight
bobbbeheedd
snoji
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eagraving
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penell draving
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colortul artictic
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illuctration
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reflected
colortul digital
digital illustration
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japazese xoala outdoor
cabrio version
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latiallatiion
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.nn porson's car
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-baing vorn
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*wihh a dog inside
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inn purple color
*with a fiin around
*a5 a hunan size scuipture
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in solid yollow color
*2ith a glass on sop
*35 hunan size statue
collapped and leaned against
in the original rideo gaze
*91th a Eair clas clip clspped
135 lego
vall
in turquosse color on a black
oato it
135 lego brick figure
compressed 1nto flat form
t-shirt
*9ihh a lot of people around
*M lego painting
containing an old photograph
in wheat color
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108 live actioon adaptation
covered with black hat cavered with decoration aabreldered cluth
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wwihh a nan propoxing to
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"itt up with nighttine
with a tovel draped over
as a papor folding art
during day with blue eky
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with external flach
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during liftoff
lyying open
as pescil draking
during night
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2ith bananas placed it
photo during Christmas night
during sumset
nnext plant
w1th big, paves cranhing
during day reflecting
next
againat shoreline
photo during
bland
photo reflecting
naking
fishing
wwtth
dark beige
filled clothea
other kitchen
leather
photoreuliutic Bd model
filled coffee
usenails
swith candies top
hat
filleed
other sunglasses
with doors open
pixel graffiti
filleed with food
other toys
with dracers open
filled with food leftovers
with fireporko
pop poster
filled vith
fuice
aninal
vith fog
protessional nascot
filled vith sugar
'ith people around
costume
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fleating sesunter
backpack
*ith people standing
costure
fleating
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billboard
vith cushion
public inctallattion
reavater
black chirt
1ith
statue
folded
chiri
*9th front sheel removed
rendered 30 model sand sculpture
folded vith other clothes
bookshelf
*ith the tongue out ornaments
fron 30 anivation
curtain
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fron drone shot
desk next Taptop
displayed top
acuiptured adible figurn
fron fully vertical
fireplace
with vavea crashing against
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cap-dovs perspective
flyer
ailhouette with
fron: lov-angle perapective
on hat
with yellov shoulaces
reflection on water
Iros: top-doin fully
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aas chotch
vertical porspective
on speater
'iithout seat cushion and pilloes
as street art zural
fron az serial perepective
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a5 a etylized digital
fron as serial vicepoint
on a t chirt with
'iithout sticky paper notes but filled with pens and rarkere
illuctration in traned
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cartocnich style
print
on table next to food
aa a stylised digital-
hanging from flover bonque:
on vbite covered Archaair
sithout aunent
illuxtration
hanging from tree branch
on white t-shirt
vorn
* =x a technical acheratic design
hanging on clothealing inn
on yellow t-shirt
vurn around a dog's 'a rack
hanging on. clothealinn ine
on clothing
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*as a toy
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*oon by a child
ea a travel poster art
hanging on drying rack
on vaterna lnn shaped dog bad
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*xx a vector clipart-atyle
hanging on folding beach
mutdoora on grass
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digital illustration
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su a vistage postcard
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Figure E9: The unique text queries of i-CIR dataset, listed in alphabetical order. These queries reflect diverse compositional transformations across appearance, context, attributes, viewpoint, and more.
43

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image quiery
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sog
10
MASS
PEPSI
piN
Viva
VhAT
PEPPI TORENE
PEPOI
Figure E10: CIRR retrieval results using Text x Image, illustrating dataset limitations. Each row shows a composed query with its top-10 retrieved results. Green boxes indicate correct retrievals, red denote incorrect ones. Several cases reveal issues with ground-truth annotations and the limited role of the image query.
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image
top
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10
SELAND
Figure E11: FashionlQ retrieval results using Text x Image, highlighting dataset limitations. Each shows composed query with image (left) and text (side). The top-10 retrieved results shown left to right. Green boxes mark correct retrievals, red boxes mark incorrect ones. The figure highlights false negatives and limited reliance on the image query.
45

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image
top
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quarry
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Figure B12: CIRCO retrieval results using Text x Image, highlighting dataset limitations. Each row shows a composed query with image (left) and text (side). The top- retrieved results are shown left to right. Green boxes mark correct retrievals, red boxes mark incorrect ones. The figure highlights false negatives and the instructional nature the text query.
46


Reviews:
[{"review_id": "KQMoyobuaI", "reviewer": "Reviewer_RdrS", "summary": "The authors propose an evaluation dataset for instance-level Composed Image Retrieval (iCIR) and propose, BASIC, and VLM-based method to support the task. The method is based on the idea of bidirectional retrieval.", "questions": "- Could the authors clarify, what is the main purpose of BASIC? Is it a model to use to help build CIR datasets or a model to perform CIR with? In the abstract it sounds like you want it to do both so I would love to clarify.\n\n- The authors state \"Existing CIR datasets often suffer from poor quality due to their construction process, i.e. two similar images are selected automatically and their difference is textually described.\" What is this statement based on? Could the authors refer to any prior work in the domain that could support this claim? Could they perhaps provide examples that illustrate their point?\n\n- (line 9) \"keep the dataset compact to facilitate future research\". Could the authors elaborate on the statement? I would argue that, while dataset quality is important, having datasets that are too compact can actually hinder research.\n\n- Beyond the modality domination assumption the authors mention in the footnote and the supplementary materials, I could not find any other limitations of their own work, neither in the paper nor in the supplementary materials; The core limitation is based on the idea of domination of one modality; how does one define such domination? And what about other limitations of this work when it comes to dataset collection process and the core assumptions the authors make there?", "limitations": "- Broader impacts are not dicussed.\n\n- Experimental setting/details: while the authors provide an overview of the models, baselines, and datasets, some important details are ommited, making it hard to understand and reproduce the results.\n\n- Results significance testing: the authors limit all the runs to a single run and did not perform any significance tests, making it harder to interpret the results.\n\n- Experiments compute resources: I could not find any information about the compute used in the experiements and the training time.\n\n- Licenses for existing assets: I could not find any information about the repositories/checkpoints the authors used.\nRelated to this, in the checklist, the authors mention not only CLIP but also BLIP, however, I could not find any information on their usage of BLIP in the paper. Was BLIP also used?\n\n- Crowdsourcing and research with human subjects: Although the authors employed human annotators to curate and filter the dataset, they did not provide any information on the annotation study, raising ethical concerns.\nThe authors did not describe to the annotators any potential risks, or obtained an IRB, which I find somewhat concerning (see checklist section 14-15 for more details)", "rating": 2, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 1, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n- The paper focuses on important problem of CIR.\n- The visualizations across the paper and the supplement are very done and do a good job illustrating the approach.\n- The authors thought about the safeguard when developing the dataset which is great.\n\n\nWeaknesses:\n\n- Open access to data and code: while the main contribution of the paper is the dataset, the dataset is not provided. It would have been very helpful to attach even a sample of dataset in the supplementary material. In addition, the authors promise to release the codebase, too, and make several claims about its content, but it is now available at the moment of review, making it hard to estimate the paper contribution.\n\n- The authors open the paper with the statement \"Composed image retrieval (CIR) combines image-to-image retrieval and text-to-image retrieval\". Perhaps I do not fully understand what they mean but this, but I am not sure I agree with this statement; In my opinion, CIR implies retrieving image, given a multmodal query, typically image+text. The bidirectional retrieval the authors mention is one of the possible ways to implement CIR models.\n\n- Related to this, the authors state \"Existing CIR datasets often suffer from poor quality due to their construction process, i.e. two similar images are selected automatically and their difference is textually described.\" Similarly, I am not sure I agree with this statement. Perhaps the authors could support their claim by referring to relevant prior work or any other motivating examples?\n\n- The authors provide an extensive ablation study, also mentioned in Table 2. While I appreciate the ablation, I do find the table a bit confusing in terms of structure. I think it would have been better to isolate each individual component to better understand its contribution. For example, authors mention that centering provides notable boost (row 2 of the table), but maybe the observed gains are due to the fact that it was the very first component that was switched on? Also, the authors mention that Harris criterion really benefits the model, but, according to the table, having it switched off still leads to very competittive results.\n\n- No clarity on motivation for instance-level CIR. To the best of my knowledge most datasets in the field and instance-level with queries centered on manipulating object properties, e.g., 'image of Eiffel tower but sparkling'.\n\n- While it is great that the authors thought about the dataset safeguards, the model release safeguards are not discussed.\n\n- Baselines:\n\t- WeiCom, one of the baselines, is not discussed in related work, making it hard to understand how it compares to BASIC.\n\t- Some relevant baselines, such as TIRG, a classical method for CIR, are excluded from the experiments.", "was_revised": true, "final_justification": "I thank the authors for the rebuttal. I think the paper represents an interesting idea and would greatly benefit from another round of revision. I encourage the authors to resubmit the paper and keep my score.", "rebuttal": "We thank **Reviewer RdrS** for the review and for explicitly recognizing strengths of our work:  \n(i) **timely problem**: CIR is an important problem,  \n(ii) **clear visual presentation** of our method, and  \n(iii) **safeguards** in the development of the **dataset**.  \nBelow, we address each of their comments in detail.\n\n---\n\n### Dataset Sample and Code Availability\n**Dataset.** A sample of the dataset is already provided. Supp. Figs. S2 (one image query per instance) and S8 (all text queries) cover most query pairs in i-CIR; Figs. 1 & 3 show composed queries with positives and hard negatives.  \n**Code.** For NeurIPS main-track papers it is permitted to release code “upon acceptance”. Full data and code will be released under MIT license immediately upon acceptance. To maximize dissemination, we will also create a **project page** with engaging visualizations and demos.\n\n### Definition of CIR &nbsp;“bidirectional retrieval”\nOur opening phrase—“CIR combines image-to-image and text-to-image retrieval → (image and text) to image retrieval”—only means that the **query is multimodal (image + text) while the database is visual**. No “bidirectional” retrieval is implied. We can rephrase in the camera-ready for clarity.\n\n### Why Existing CIR Datasets Can Be Noisy\nThe construction protocols of CIRR, CIRCO, and FashionIQ rely on automatic selection of visually or semantically similar image pairs, followed by the annotation of a textual description that highlights their differences. The poor quality of CIRR and FashionIQ is well known and has led to refined-CIRR/FashionIQ (CoLLM, CVPR 2025), as mentioned by Reviewer rzCM. We refer explicitly to the construction details provided in the original papers:\n\n* **CIRR** (§4.1): ``We first form image pairs then collect related annotations by crowd-sourcing. The pairs are drawn from subsets of images, as described below... We use the popular NLVR2 dataset... Prior to forming reference-target image pairs, we construct multiple subsets of six images that are semantically and visually similar, denoted as S... Here, to construct a subset, we randomly pick one image from the large corpus. We then sort the remaining images in dataset by their cosine similarity to this image using ResNet152 image feature vectors pre-trained on ImageNet... Within each constructed image subset S, we draw nine pairs of images... We collect a modification sentence for each pair of reference-target images using Amazon Mechanical Turk (AMT).''.  \n* **CIRCO** (§4): ``...We introduce an open-domain benchmarking dataset named Composed Image Retrieval on Common Objects in context (CIRCO). It is based on real-world images from COCO 2017 unlabeled set and is the first dataset for CIR with multiple ground truths. Contrary to CIRR, we start from a \\emph{single pair of visually similar images and write a relative caption}... Then, we propose to employ our approach to retrieve the top 100 images according to the query and combine them with the top 50 images most visually similar to the target one. Finally, we select the images that are valid matches for the query.''. \n* **FashionIQ** (§3): \"The images of fashion products that comprise Fashion IQ were originally sourced from a product review dataset... For each image, we followed the link to the product website available in the dataset, in order to extract corresponding product information, when available... To ensure that the relative captions can describe the fine-grained visual differences between the reference and target image, we leveraged product title information to select similar images for annotation with relative captions.\".\n\nThis methodology often yields:  \n(i) pairs lacking meaningful fine-grained differences (supp. Fig. S10, rows 4–5);  \n(ii) text-only queries sufficing to find the target (supp. Fig. S9, row 4; quantitative in rebuttal Table in the answer to Reviewer rzCM);  \n(iii) negative pools containing false negatives (supp. Fig. S9, row 6).\n\nOur semi-automatic pipeline with expert annotators yields a **compact 300 K-image benchmark that is as hard as adding 20M random LAION distractors** (supp. Fig. S5).\n\n### Ablation Table Structure & Harris Regularization\n\nTable 2 is cumulative by design: we switch BASIC’s components on to illustrate how performance builds up through a **logical sequence** of design steps. **Ordering is not arbitrary**: Some components *depend* on the presence of others to be *effective* (e.g. projection assumes centred features, Harris requires min-normalised scores). At the reviewer's request, we also report a leave-one-out ablation (Table below), but note the expected caveats: removing min-norm or centering while leaving dependent steps active naturally hurts results. We hope this expanded analysis provides a clearer understanding of each component’s role and justifies the original cumulative layout of Table 2. Finally, Harris is a novel contribution to CIR that brings consistent improvements over all cases, even if its gains may appear more modest compared to other components. Its strength is aligned with the goal of CIR, as it reinforces retrievals that satisfy both the image and the text query, rather than favouring one modality over the other.\n\n| Centering | Min Norm. | Harris | Context. | Proj. | Q. Exp. | ImageNet-R | NICO | MiniDN | LTLL | i-CIR |\n|-----------|-----------|--------|----------|-------|---------|-----------:|-----:|-------:|-----:|------:|\n| ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | **32.13** | **31.65** | **39.58** | **41.38** | 47.39 |\n| ✗ | ✓ | ✓ | ✓ | ✓ | ✓ | 23.29 | 19.93 | 24.87 | 10.16 | 37.85 |\n| ✓ | ✗ | ✓ | ✓ | ✓ | ✓ | 18.83 | 19.93 | 24.67 | 4.36 | 42.14 |\n| ✓ | ✓ | ✗ | ✓ | ✓ | ✓ | 30.75 | 29.82 | 38.85 | 40.65 | 46.11 |\n| ✓ | ✓ | ✓ | ✗ | ✓ | ✓ | 26.18 | 30.61 | 33.64 | 34.50 | 39.74 |\n| ✓ | ✓ | ✓ | ✓ | ✗ | ✓ | 17.31 | 13.96 | 21.22 | 22.42 | 27.21 |\n| ✓ | ✓ | ✓ | ✓ | ✓ | ✗ | 27.54 | 28.90 | 35.75 | 38.22 | **47.73** |\n\n### Motivation for Instance-level CIR\nWe kindly *disagree* with the reviewer. Even though the example that the reviewer provides, mentions the Eiffel tower → is an instance-level one, **existing datasets follow a semantic-level definition** (we cannot guarantee that a tiny fraction of instance-level queries does not exist, since we have not manually inspected the whole datasets). Examples from original papers:  \n(i) CIRCO [2] Fig. 7 retrieves *different* lunch box, clock, horse, etc.;  \n(ii) CIRR [23] Figs. 1&4 retrieve *different* dog or carriage;  \n(iii) FashionIQ [35] Fig. 3 retrieves a *different* T-shirt/blouse;  \n(iv) ImageNet-R (Pic2Word) [30] Fig. 3 retrieves *any* fish/tiger/shark matching the style cue.  \nInstance-level CIR therefore **fills a genuine gap**.\n\n### “Model-release safeguards”\nWe release **no new model weights**; BASIC is training-free (regarding composed retrieval) and it uses public OpenCLIP/SigLIP checkpoints. Only the original licences apply.\n\n### Baselines\n* **WeiCom** = convex combination of normalised image-to-image and text-to-image similarities (λ ∈ [0, 1]). We shall add.     \n* **TIRG** requires supervised triplet training, so it is not a zero-shot baseline.\n\n### Purpose of BASIC\nBASIC is **purely a retrieval method**; it is **not** used in dataset construction.\n\n### “Compact Yet Hard” Dataset\nThanks to curated visual, textual, and composed hard negatives, a 300K-image i-CIR database matches the difficulty of 20M LAION distractors. Extracting CLIP ViT-L features: 1 h 25 m on a single V100 vs. ≈ 4 days for 20 M images. Therefore experimentation and benchmarking is **significantly sped up** while **keeping the difficulty of the dataset high**.\n\n### Limitations\n**Methodology-wise**, we already mention two limitations: modality-domination and cases where text query takes the form of an instructional or relational phrasing (in which training-free methods can underperform). Both are further explored in this rebuttal; see responses to Reviewer rzCM and Reviewer pyz4, respectively. Throughout the manuscript we also mention other limitations, such as the computational overhead of contextualization and the construction process of corpora via ChatGPT. **Dataset-wise**, our semi-automatic pipeline trades some speed for fidelity: every composed query is manually vetted (e.g. for hard negatives, licence/safety issues, etc.), so building i-CIR is slower and costlier than fully automatic datasets but yields an **ambiguity-free** benchmark the field currently lacks (Reviewer rzCM). Moreover, the design mostly assumes a single salient object per image query and uses English-only phrasing with public-domain imagery. We shall add.\n\n### Experimental Details\n* Determinism: BASIC has **no trainable parameters**; single runs suffice.  \n* Compute: see supp. Fig. S4 for exact timings.  \n* Checkpoints/licences: OpenCLIP ViT-L/14 and SigLIP ViT-L/16 (Apache-2.0). BLIP not used—checklist typo.\n\n### Crowdsourcing & IRB\nWe appreciate the reviewer’s concern regarding ethical practices involving human annotators. To clarify, the human annotators in our study were **employed** (salaried institution employees) **solely for the task of labelling and curating images** for a new instance-level composed image retrieval dataset. The annotators were trained workers performing a well-defined annotation task. **Our research is not performed on human subjects**. No personal or sensitive data, such as images of people, faces, license plates, or other identifiable features, were included in the dataset. As such, our use of annotators falls under standard data labelling practices and *does not meet the criteria for human subjects research that would require IRB oversight*. We will update the paper and the checklist to clarify these points and avoid any misunderstanding regarding ethical compliance."}, {"review_id": "F1SvNWioS8", "reviewer": "Reviewer_przL", "summary": "This paper provides a new benchmark dataset for composed image retrieval task called $i$-CIR, which contains each query with visual/textual/composed hard negative samples, making it as challenge as randomly selected distractor samples datasets four orders of magnitude larger. The author also come up with a new composed image retreival method called BASIC which take both query-image-to-image and query-text-to-image similarities into account. Without the necessery of any training process, BASIC beats sota models on $i$-CIR and other CIR datasets.", "questions": "### Questions\n- Line 122 mentions \"another objects of the same category\", how are the objects categoried?\n- In Line 127~135 the author introduces the process to generate query, positive and hard negative samples with the help of annotators. However, in Line 132 \"Finally, annotators manually select images within the visual hard negatives to serve as image queries.\" makes me confusing. Why is the hard negatives are select as image queries, as all composed positives have already picked up from hard negative pools. What's left must mismatch the seed image/seed sentence.\n- In Line 228~230 the author describes how corpus $C_+$ is generated from single-word text queries. Is this process also require the help of LLM or is done by human or fixed templates?\n\n### Suggestions\n- Besides the current information metioned in the main text, it is better to add the details on how ChatGPT is used in supplementary materials, including prompts used and several real QA samples.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 2, "strengths_and_weaknesses": "### Strengths:\n- The paper is well orgainzed and the author provides details on the building process of the benchmark datsets. \n- Through expriments are carried out on sota CIR methods and the results demostrates the effectiveness of BASIC method on CIR task.\n- Compared with other CIR benchmarks, $i$-CIR is more challenging and will contributes to the further development in CIR task.\n- Though not new models/training schemes/dedicated losses are designed, the paper dive into current available methods and proposes a new training-free framework which beats all sota models in CIR task.\n### Weaknesses\n- The code is not available until acceptance.\n- Supplemental material is not provided, and we fail to check these details mentioned in main text, such as Line 164, Line 244, Line 307, Line 330 and Line 340. \n- There exists some typo\n  - Firuge 2(a) descriptions should be \"image queries\" \n  - Line 129 \"and composed hard negatives for cases (i & ii), (ii), and (iv & v), respectively.\" the second (ii) should be (iii) \n  - Line 279 \"a = 0.2\" here $a$ maybe refer to $\\alpha$ in Eq(2) ?\n- Multiple-step of the generation of $i$-CIR benchmark relies on the involvement of human annotators, which may hinder the scaling up of the benchmark.", "was_revised": true, "final_justification": "Most of the problems come with my ignorance of the suplementary materials. And since these are not problems, I improve the clarity score from 2 to 3.\nOther problems are well explained in the rebuttal and I am satisfied with the explainations. So I will keep the remaining scores unchanged.", "rebuttal": "We thank **Reviewer przL** for the review and for explicitly recognising several key strengths of our work:  \n(i) the paper is **well organised** and explains the full benchmark-building pipeline,  \n(ii) **thorough experiments** on strong baselines demonstrate the **effectiveness of BASIC** on CIR task,  \n(iii) i-CIR is **clearly more challenging** than existing CIR datasets and will **“contribute to further development”** in CIR task, and  \n(iv) a training-free framework that nevertheless **beats the SOTA** is practically valuable. \n\nMany of the points that the reviewer could not verify *are clarified in the supplementary material* (available via the *ZIP* link on the OpenReview page). We understand that its location *may not have been immediately obvious* and kindly invite the reviewer to consult it. Below, we provide point-by-point responses to all remaining comments.\n\n-----\n\n### Code Release\nFor NeurIPS main-track papers it is permitted to release code **“upon acceptance”**. About the public release policy, full data and code will be released under MIT license immediately upon acceptance. We wish to maximize the dissemination and usage of our dataset and method, thus we will also create a **project page** with engaging visualizations and demos.\n\n### Supplementray Material is Not Provided / Wrong Statement → It Is Provided\nThe full supplementary material—containing additional figures, tables, implementation details, and further discussion—is available via the *ZIP* link on the OpenReview page. We kindly invite the reviewer to consult it, as it addresses the points noted on lines 164, 244, 307, 330, 340, and others.\n\n### Typos & Minor Edits\nWe will fix in the camera-ready version of the paper.\n\n### Human Annotation vs. Scalability\n**High-quality**, instance-level composed image retrieval ``labels'' cannot be obtained purely automatically, especially given (i) the exhaustive and explicit collection and curation of visual, textual, and **composed hard negatives**, and (ii) the **copyright-safe** and **sentitive-content-safe** approach we decided to follow. In instance-level image-to-image retrieval, datasets like GLDv2 (Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrieval, CVPR 2020) that are created automatically with crowd-sourced labels are *known to contain a high amount of noise*. Additionally, composed image retrieval has an extra complexity in creating a dataset. \nIn CIR, alternative shortcuts in CIRR and FashionIQ result in **well-known weaknesses**, which are explicitly mentioned in our paper (subsection 3.4). These exact weaknesses are *known in the community* and have recently resulted (CoLLM: A Large Language Model for Composed Image Retrieval, CVPR 2025) in the introduction of a *refined version* of them (refined-CIRR, refined-FashionIQ), as mentioned by Reviewer rzCM. We **perform experiments on these refined datasets** as an answer to Reviewer rzCM and observe a *performance boost* of up to 13.7% R@10 on Fashion-IQ Shirt for our method.\nOur semi-automatic pipeline involving expert annotators yields a compact set that is as hard as adding 20M random LAION distractors (supplementary Fig. S5). As a compact benchmark, i-CIR **lowers compute barriers** and **encourages fast iteration**, but **without sacrificing difficulty**. For instance, extracting CLIP ViT-L features for the 300K-image i-CIR takes merely 1 h 25 min on a single NVIDIA V100 (32 GB), whereas processing a 20M-image distractor set would extend to almost four days. Thus, *scaling up is not hindered*. We believe that this trade-off is *preferable* for future benchmarks.\n\n### Category Hierarchy\nSupp. Fig. S1 shows our 3-level taxonomy. “Same category” in Line 122 refers to level-3 (the most specific) categories.\n\n### Why a Visual Hard-Negative Can Become an Image Query / All Composed Positives Have Already Picked Up from Hard Negative Pools\nThe composed positives are picked from the overall **candidate image pool** (L115-116), which consists of potential queries, positives and (hard) negatives. There are three types of hard negatives: visual, textual, and composed (L130-132). Visual hard negatives include two cases: (i) the same instance under the same or different conditions; (ii) a very similar but different instance. Images of type (i) make excellent alternative image queries. Annotators therefore pick a subset of these and drop all original seed images.\n\n### Corpus Construction & GPT Usage\nSection S2 of the supplementary material already describes the prompt we used with ChatGPT to generate the positive word corpus ($C_+$), which consists of object-centric terms used to guide semantic projection. However, *due to an oversight*, the corresponding prompt for generating the negative word corpus ($C_-$), which captures stylistic and contextual variations, was not included. We *thank the reviewer for pointing this out*. Below, we provide the exact prompt used:\n\n```We are working on a composed image retrieval task, where the input consists of an image and a text. The goal is to retrieve the most relevant matching image from a database. Typically, the image contains a main object in a particular “state”. This state can refer to a style (e.g., retro, handwritten, futuristic) or a contextual setting (e.g., next to the sea, on the beach, beside a table). For example: a retro mug, a cat next to a table, a handwritten notebook. We would like you to generate a .txt file containing 100 possible states in which an object can be found. Each line in the file should contain a single state expressed in natural language. Wait for me to say “next” before giving the next batch of states.```\n\nHere are the first 10 outputs of such a command:\n\n```covered in snow\non a wooden shelf\nfloating in water\nwrapped in plastic\npainted with graffiti\nnext to a fireplace\nsitting on grass\nsurrounded by candles\nhanging from a tree\nunder a spotlight\n```\n\nTo support transparency and reproducibility, we will include this prompt in the revised supplementary material and provide the exact corpora used as part of the released codebase."}, {"review_id": "cbQZwG9uyP", "reviewer": "Reviewer_rzCM", "summary": "This paper makes two significant contributions to composed image retrieval: a new benchmark and a training-free approach. The large-scale, high-quality benchmark effectively addresses ambiguity issues present in prior datasets, benefiting from a well-designed semi-supervised construction pipeline with human verification. The proposed training-free method, utilizing late-interaction between image and text features, achieves remarkable performance across various benchmarks, even outperforming several training-based methods. Extensive ablation studies and experiments further reinforce the paper's claims and contributions.", "questions": "1. The authors should include the performance of some strong fine-tuning methods on large-scale datasets, especially those leveraging better text encoders, such as CoVR-2 [a] and MCL [b]. This would provide a more comprehensive comparison with state-of-the-art training-based approaches.\n2. In Figure S9, a significant portion of the top-1 retrieval results appears to be the reference image itself. This observation seems to contradict the explanation in Line 245, \"text query often provides sufficient information to resolve the task independently.\" Could the authors elaborate on the specific challenges or limitations of their solution on popular benchmarks (CIRR, Fashion-IQ, CIRCO) that lead to this phenomenon? While acknowledging the known issues with these datasets, the extremely low performance of \"BASIC\" on these benchmarks raises concerns about the trustworthiness and generalizability of the proposed solution. Furthermore, given that CoLLM [c] introduces refined versions of the CIRR and Fashion-IQ benchmarks, it would be valuable to evaluate the method on these refined datasets to determine if similar issues persist.\n3. What are the speed and storage requirements of “BASIC” during retrieval, especially when compared to a single dot-product operation? Providing these metrics would offer crucial insights into the practical efficiency and deployability of the proposed training-free method.\n\n\n[a] Ventura, Lucas, et al. \"CoVR-2: Automatic Data Construction for Composed Video Retrieval.\" TPAMI 2024.\n\n\n[b] Li, Wei, et al. \"Improving context understanding in multimodal large language models via multimodal composition learning.\" ICML. 2024.\n\n\n[c] Huynh, Chuong, et al. \"Collm: A large language model for composed image retrieval.\" CVPR. 2025.", "limitations": "Yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 4, "strengths_and_weaknesses": "## Strengths\n### Quality\nThe paper presents a comprehensive and complete body of work, supported by extensive experiments and detailed supplementary material. The authors demonstrate a thorough understanding of previous research, providing strong empirical evidence to validate their hypotheses. For the dataset construction, it provides many examples and statistics to show the strengths of the new benchmark. The construction details are also clearly discussed. The new simple but effective baseline is explained clearly and reasonable for the problem when both image and text should be considered.\n### Clarity\nThe paper is exceptionally well-written, complemented by clear and informative figures and tables that effectively highlight its contributions. The narrative flow is easy to follow, enhancing overall readability. The pipeline figure is very easy to understand and connected to the text in Sec 4. Moreover, many examples and additional analysis on both model and dataset are included in the supplementary\n### Significance\nBoth the proposed benchmark and solution offer significant contributions to the community. The new benchmark is particularly impactful, addressing critical ambiguity issues in prior datasets that have historically undermined the trustworthiness of existing methods. Furthermore, the simple yet effective training-free solution opens a promising new direction for research in late-interaction methods within CIR.\n### Originality\nThis work demonstrates novel insights through both its evaluation strategies and its innovative training-free solution for very challenge topic - CIR. It clearly reflects the authors' advanced understanding of the field, pushing the boundaries of current approaches.\n\n\n## Weaknesses\n### Quality\nWhile comprehensive, the work could benefit from the inclusion of additional experiments to further bolster its completeness. (See questions)\n### Significance\nDespite the proposed solution's efficiency and the acknowledged discussion of its limitations, the modest performance gains on key benchmarks such as CIRR, Fashion-IQ, and CIRCO represent a critical concern regarding its overall impact and practical utility.", "was_revised": true, "final_justification": "After reading the rebuttals and other reviews, I believe the paper meets the acceptance threshold for the conference. Other reviewers' main concerns are about the reproducible and the ethical issues during reconstructing the benchmark. However, I believe the authors already explain during their rebuttals. I keep my initial score for this paper.", "rebuttal": "We thank **Reviewer rzCM** for a **very positive** assessment and for highlighting that:  \n(i) the i-CIR construction pipeline is **transparent and well-motivated**,  \n(ii) the work is backed by a **comprehensive experimental suite** and rich **supplementary** material that provide **strong empirical evidence for every claim**,  \n(iii) the manuscript is **exceptionally clear**—with intuitive figures (particularly the section 4 pipeline) and plentiful illustrative examples—making the narrative **easy to follow**, and  \n(iv) the combination of an **ambiguity-free benchmark** and a **simple yet effective training-free BASIC method** represents a **significant and original step** forward for late-interaction approaches in composed image retrieval.   \nBelow we address the remaining suggestions and concerns:\n\n---\n\n### Comparison with Additional SOTA Training-Based Methods\n\nWe appreciate the reviewer’s suggestion to include *recent training-based methods*. Using the authors’ official code and checkpoints, we evaluated CoVR-2 and MCL on the five CIR benchmarks of Table 1 of the main paper. As shown in the table below, our *training-free* BASIC still achieves the **best mAP on every dataset**—often by a sizeable margin (e.g. +20.6% on ImageNet-R and +10.5% on i-CIR). These results strengthen the evidence that BASIC offers competitive accuracy without the computational cost of training or fine-tuning.\n\n| Method (venue) | ImageNet-R | NICO++ | MiniDN | LTLL | *i*-CIR |\n|---------------|-----------|--------|--------|------|---------|\n| CoVR-2 (TPAMI 2024) | 11.52 | 24.93 | 27.76 | 24.68 | 36.94 |\n| MCL (ICML 2024) | 8.13 | 19.09 | 18.41 | 16.67 | 24.41 |\n| **BASIC (ours)** | **32.13** | **31.65** | **39.58** | **41.38** | **47.39** |\n\n---\n\n### Why Gains Are Smaller on CIRR, FashionIQ, and CIRCO\n\n**1:** These three benchmarks suffer from modality domination: many queries are answerable from the *text alone*. To make the point quantitative, we swept a mixing weight \\( λ ∈ [0,1] \\) between text-only \\( λ = 0 \\) and image-only \\( λ = 1 \\) similarity for three naïve fusion methods (WeiCom, Sum, and Product). The average (over three fusion methods) peak–vs.–unimodal gap is a good proxy for how much a dataset *rewards composition*. On i-CIR the average gap is enormous: **+16.1 mAP (+391% relative)**, whereas it shrinks to +3.0 mAP (+11%) on CIRR, +5.0 mAP (+26%) on FashionIQ, and +6.8 mAP (+167%) on CIRCO. Moreover, the highest unimodal performance is always when \\( λ = 0 \\), i.e. text-only. Thus legacy datasets reward composition only marginally, whereas *i*-CIR demands strong cross-modal synergy; BASIC is designed for the latter scenario. Detailed results are presented on the Table below.     \n**2:** Sensitivity to relational or instructional phrasing (link to **Reviewer pyz4**). As shown in our answer to **Reviewer pyz4**, rewriting just *ten* highly relational CIRR queries into concise, absolute descriptions boosts BASIC from **18.0% → 44.4% mAP** and from **10% → 40% R@1** (Table 2 in that response). This confirms that the low scores stem largely from the relational wording prevalent in those datasets rather than from a fundamental weakness in composition.  \n**3.** Following the reviewer’s suggestion, we evaluate BASIC and BASIC without Harris regularization (BASIC\\*) on the newly released CoLLM-Refined versions of CIRR and Fashion-IQ (second Table below) . Both variants show notable performance gains on the refined datasets (**up to 13.7% R@1** on refined-Fashion-IQ Shirt). However, it’s important to highlight that the CoLLM refinements primarily aim to *reduce annotation ambiguities* in CIRR and Fashion-IQ—this addresses a different challenge than modality dominance (evidenced by BASIC\\* outperforming BASIC) or CLIP’s sensitivity to specific types of phrasing, such as instructional or relational queries.\n\n| λ | **WeiCom CIRCO** | **WeiCom CIRR** | **WeiCom FIQ** | **WeiCom i-CIR** | **Sum CIRCO** | **Sum CIRR** | **Sum FIQ** | **Sum i-CIR** | **Prod CIRCO** | **Prod CIRR** | **Prod FIQ** | **Prod i-CIR** |\n|--------------|-----------------|-----------------|---------------|------------------|--------------|--------------|-------------|---------------|---------------|---------------|--------------|---------------|\n| 0   | 4.00 | 26.09 | 18.94 | 4.11 | 4.10 | 26.65 | 18.95 | 4.10 | 4.10 | 26.66 | 18.95 | 4.10 |\n| 0.1 | 10.84 | 20.86 | 18.47 | 17.94 | 7.00 | 30.70 | 22.52 | 8.89 | 5.30 | 28.36 | 20.29 | 5.24 |\n| 0.2 | 10.84 | 19.98 | 17.80 | 20.94 | 10.30 | 31.23 | 25.69 | 18.30 | 6.49 | 30.08 | 21.39 | 7.63 |\n| 0.3 | 11.07 | 19.60 | 17.79 | 21.39 | 8.14 | 27.40 | 26.95 | 15.97 | 9.11 | 31.19 | 22.86 | 11.37 |\n| 0.4 | 11.10 | 19.51 | 17.42 | 20.72 | 6.93 | 22.48 | 24.71 | 9.61 | 10.35 | 30.73 | 24.63 | 16.83 |\n| 0.5 | 10.95 | 18.86 | 16.91 | 19.48 | 5.61 | 19.02 | 20.83 | 6.04 | 11.12 | 29.37 | 25.95 | 20.78 |\n| 0.6 | 10.99 | 18.31 | 16.28 | 17.52 | 4.57 | 16.26 | 16.22 | 4.11 | 10.50 | 26.33 | 25.94 | 19.24 |\n| 0.7 | 10.52 | 17.99 | 15.57 | 14.77 | 3.82 | 14.61 | 12.54 | 3.09 | 7.61 | 22.18 | 24.06 | 13.51 |\n| 0.8 | 10.77 | 17.22 | 14.49 | 11.70 | 3.16 | 13.46 | 10.24 | 2.56 | 5.75 | 18.05 | 19.48 | 7.99 |\n| 0.9 | 10.55 | 16.62 | 12.70 | 6.98 | 2.86 | 12.49 | 8.66 | 2.28 | 3.93 | 14.45 | 12.60 | 4.06 |\n| 1.0 | 3.26 | 11.56 | 7.69 | 2.07 | 2.61 | 11.73 | 7.69 | 2.07 | 2.61 | 11.73 | 7.72 | 2.07 |\n\n| Dataset | Split | Method Variant | mAP | R@1 | R@5 | R@10 | R@50 |\n|---------|-------|----------------|-----|-----|-----|------|------|\n| **CIRR** | Legacy | BASIC | 24.34 | 15.83 | 40.89 | 53.90 | 82.27 |\n| | Refined | BASIC | **28.28** | **21.87** | **48.96** | **60.82** | **86.66** |\n| | Legacy | BASIC\\* | 26.89 | 17.98 | 44.92 | 58.80 | 86.51 |\n| | Refined | BASIC\\* | **31.89** | **25.30** | **53.49** | **65.94** | **90.15** |\n| **Fashion-IQ Dress** | Legacy | BASIC | 6.96 | – | – | 18.59 | 37.68 |\n| | Refined | BASIC | **9.91** | – | – | **24.40** | **47.63** |\n| | Legacy | BASIC\\* | 7.43 | – | – | 19.98 | 39.86 |\n| | Refined | BASIC\\* | **11.27** | – | – | **28.59** | **52.43** |\n| **Fashion-IQ Shirt** | Legacy | BASIC | 10.93 | – | – | 26.30 | 44.80 |\n| | Refined | BASIC | **17.15** | – | – | **36.82** | **56.09** |\n| | Legacy | BASIC\\* | 12.93 | – | – | 29.88 | 47.06 |\n| | Refined | BASIC\\* | **22.55** | – | – | **43.54** | **62.40** |\n| **Fashion-IQ Toptee** | Legacy | BASIC | 10.28 | – | – | 23.92 | 40.95 |\n| | Refined | BASIC | **14.57** | – | – | **32.08** | **51.41** |\n| | Legacy | BASIC\\* | 12.08 | – | – | 26.21 | 44.57 |\n| | Refined | BASIC\\* | **19.08** | – | – | **38.27** | **57.98** |\n\n---\n\n### Speed and Storage Footprint\n\nWe consider Text × Image (“Product”) as the minimal CIR baseline, requiring only dot-products between the CLIP-encoded queries and the precomputed image features. BASIC builds on this with lightweight components—all applied at query time only.\n\nThree components introduce **non-negligible overhead**:\n\n* **Semantic Projection:**  \n  Requires storing a d × k projection matrix and applying it to the query, with both time and storage complexity of O(dk).\n\n* **Query Expansion:**  \n  In our current implementation, this involves a secondary retrieval over top-Q items (O(QN)), along with a projection step of O(dk). This step can be made efficient using the FAISS library.\n\n* **Contextualization:**  \n  This is the most computationally expensive component, requiring one extra (batched) forward pass through the CLIP text encoder, followed by averaging.\n\nOther components—*centering*, *min-based normalization*, and *Harris fusion*—have trivial cost (O(d) or less) and negligible storage overhead.\n\nTotal runtime on an NVIDIA A100 40 GB GPU:\n\n| Method | Runtime (ms) |\n|---------|--------------|\n| BASIC | 61.9 |\n| –Contextualization | 25.9 |\n| –Contextualization –Q.Exp. | 25.8 |\n| –Contextualization –Q.Exp. –Projection | 25.6 |\n| Product | 25.5 |\n\nAs shown, all components except contextualization add *negligible latency*, with total runtime increasing only from 25.5 ms to 25.9 ms. Contextualization, which involves CLIP forward passes, is the dominant factor—accounting for most of the 61.9 ms cost in the full BASIC pipeline (as shown also in Supplementary Figure S4).  \nTo mitigate this, a key future direction is to develop a *lightweight embedding network on top of CLIP* that approximates contextualized representations without runtime overhead."}, {"review_id": "21bYaEDWM1", "reviewer": "Reviewer_pyz4", "summary": "This paper introduces i-CIR, a new instance-level composed image retrieval benchmark designed to better reflect real-world retrieval scenarios where a query combines both an image and a text-based modification. Unlike existing CIR datasets that often rely on class-level semantics or automatically constructed triplets, i-CIR emphasizes retrieving images of the same object instance under textual modifications, making it both compact and challenging. To address the lack of training data, the authors propose a training-free method called BASIC, which leverages pre-trained vision-language models to separately compute image-to-image and text-to-image similarities, followed by a fusion step. The approach sets new state-of-the-art performance on i-CIR and other CIR datasets, demonstrating strong composition abilities without requiring additional training.", "questions": "1.The proposed BASIC method relies entirely on fixed heuristics and manually designed components (e.g., projection, score fusion), without any learnable modules. How would the method adapt to domains that differ significantly from those seen during CLIP pretraining? Have the authors considered hybrid or lightly fine-tuned variants?\n2.The dataset appears to include mainly short or mid-length modification texts. Could the authors comment on how their method would handle longer, more compositional or relational queries that require fine-grained semantic understanding?\n3.Most text queries are phrased at attribute or scene level. Is the model capable of handling relational or comparative queries, e.g., “the same object as in A but rotated and with a different texture”?\n4.The experiments do not include comparisons against more recent CIR methods from 2024–2025. Are there any strong baselines published after 2023 that could be added to better position the contribution?\n5.While the projection and fusion modules are effective, they depend on manually selected corpora and static subspaces. Could the authors clarify how robust these choices are across different instances and domains? Is there a way to make them more adaptive?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths: \n1.The motivation of this paper is clear. Figures 1 and 3 powerfully demonstrate the deficiencies of the existing CIR datasets in instance-level definition and negative sample construction, highlighting the necessity and challenge of constructing the i-CIR dataset. Section 3 elaborates on the complete process from instance definition to negative sample screening, demonstrating a high level of annotation quality and engineering controllability. \n2.Instance-level CIR is closer to real application scenarios such as product search. The BASIC method proposed by the author does not require training, and the fusion strategy is concise and efficient, especially suitable for scenarios with low resources or high deployment flexibility requirements.\n3.Performance evaluations on multiple CIR benchmarks (such as i-CIR, ImageNet-R, MiniDN, LTLL) demonstrate the method's wide applicability and stable performance across different categories and tasks. The ablation experiments in Table 2 clearly verified the effectiveness of key modules such as text contextualization and semantic projection, demonstrating good design logic and module contributions.\nWeakness:\n1.The proposed BASIC method relies entirely on fixed heuristics and manually designed components (e.g., projection, score fusion), without any learnable modules. How would the method adapt to domains that differ significantly from those seen during CLIP pretraining? Have the authors considered hybrid or lightly fine-tuned variants?\n2.The dataset appears to include mainly short or mid-length modification texts. Could the authors comment on how their method would handle longer, more compositional or relational queries that require fine-grained semantic understanding?\n3.Most text queries are phrased at attribute or scene level. Is the model capable of handling relational or comparative queries, e.g., “the same object as in A but rotated and with a different texture”?\n4.The experiments do not include comparisons against more recent CIR methods from 2024–2025. Are there any strong baselines published after 2023 that could be added to better position the contribution?\n5.While the projection and fusion modules are effective, they depend on manually selected corpora and static subspaces. Could the authors clarify how robust these choices are across different instances and domains? Is there a way to make them more adaptive?", "was_revised": true, "final_justification": "After carefully reading the author's response, for Q1, the authors carefully analyzed the effectiveness of the Tran-free approach and proposed a more targeted and compatible expansion direction, namely exploring hybrid text embedding strategies. I strongly agree with the author's analysis. For Q2 and Q3, the authors conducted a controlled study, automatically converting the original query into a longer, relational, and imperative form. Although BASIC is a training-free method, it demonstrates good robustness across variants compared to the baseline, validating its generalization ability in natural language transformation. For Q4, the authors provided additional experiments, strengthening the persuasiveness of their article. For Q5, the authors demonstrated the cross-domain robustness of their universal projection corpus. Overall, the author provided a clear answer, which resolved my confusion and enhanced the persuasiveness of the article.", "rebuttal": "We thank **Reviewer pyz4** for the review and for explicitly recognising several core strengths of our submission:  \n(i) a **clear motivation** punctuated by Figs. 1 & 3 and the full annotation pipeline in section 3,  \n(ii) instance-level CIR is a **real-world task**,  \n(iii) BASIC is a training-free method with a **concise and efficient fusion strategy**, especially suitable for scenarios with low resources or high deployment flexibility requirements,  \n(iv) **strong, broad empirical results** across i-CIR, ImageNet-R, MiniDN, and LTLL,  \n(v) ablations in Table 2 **verify the logic and value of each component** of BASIC.\n\n---\n\n### Adaptability of a Fixed, Training-Free BASIC\n\nBASIC purposefully avoids task-specific learning so that it *inherits* CLIP’s open-domain coverage instead of narrowing it down. BASIC relies on manually designed components, each *motivated by intuitive principles*: semantic projection isolates object-level semantics, Harris fusion balances dual-modality relevance, and contextualization mitigates CLIP’s sensitivity to phrasing. These components are carefully chosen to remain training-free and interpretable, achieving SOTA performance across *diverse and shifted domains* and showing *strong robustness* without requiring adaptation. Nonetheless, as the reviewer rightly notes, BASIC also inherits CLIP’s limitations—especially in the textual domain, where phrasing, abstraction level, or relational structure can affect performance. For visual inputs, CLIP’s vast pre-training set offers strong coverage. While fine-tuning could improve in-domain performance, it risks reducing generality. \n\nA more targeted and compatible extension would be to explore **hybrid text embedding strategies**. For example, training a lightweight text encoder for relational or instructional queries, related to the following reviewer’s comment, or fine-tuning a contextualization-like embedding to reduce sensitivity and inference overhead. These directions remain promising extensions that target specific constraints (e.g., CLIP sensitivity to query wording or time overhead of contextualization).\n\n---\n\n### Longer, Relational, or Instructional Text Queries\n\nThank you for the comment, which led us to the following *insightful analysis*. We assume that a text query can be almost equivalently formulated in different ways, e.g. **concisely** (“engraving”), in a **longer** sentence (“as a vintage stylish engraving”), in a **relational** way (“the same landmark as the one depicted in the image but as an engraving”), and in an **instructional** way (“engrave the landmark depicted in the image”). In the context of i-CIR, we refer to those cases as *original*, *longer*, *relational* and *instructional*. The queries of i-CIR are brief and comprehensive, while existing datasets like CIRR and CIRCO are often relational or/and instructional, while having larger lengths.\n\nTo probe BASIC’s robustness against such text query styles we automatically rewrite (via ChatGPT) all text queries of i-CIR, evaluate mmAP and present the results in the Table below (Δ is the relative change with respect to the original row). This is an automated process that allows us to perform such an insightful experiment despite the possibility of noise. Additionally, we perform the reverse mapping for 10 relational queries of CIRR that are converted into shorter and absolute descriptions. Results are discussed below.\n\n| Text-query Variant | BASIC mmAP | BASIC Δ % | FreeDom mmAP | FreeDom Δ % | MagicLens mmAP | MagicLens Δ % |\n|--------------------|-----------:|----------:|-------------:|------------:|---------------:|--------------:|\n| Original (concise) | 47.39 | —  | 25.14 | —  | 32.00 | — |\n| Longer             | 45.02 | −5 | 23.91 | −5 | 26.90 | −16 |\n| Relational         | 38.95 | −18| 18.99 | −25| 29.34 | −8 |\n| Instructional      | 37.47 | −21| 18.31 | −27| 28.85 | −10 |\n\n**Longer.** We expand all text queries by adding 1–4 extra words while preserving their meaning. On average the phrases grow from 4.4 → 7.9 words—an increase of 3.5 words or +79 %, effectively almost doubling their length. The median rises from 4 to 8 words, the shortest query goes from 1 to 4 words, and the longest from 13 to 15. Example: “crowded” becomes “in a dense and crowded scene”. The ChatGPT prompt used to convert the text queries is: \n\n```Take each text query and, without altering its core meaning or introducing new concepts, enrich it with tasteful descriptive words or clarifying phrases so that its length is roughly doubled. Preserve the original order and casing and return each pair as ‘original query’ : ‘augmented longer query’ with no additional commentary. Example: ‘crowded’ → ‘in a dense and crowded scene’``` \n\nWe observe that BASIC has a *small drop in performance* similar to that of FreeDom, while MagicLens has a *larger drop*, therefore revealing a new advantage of training-free (relying on CLIP) methods. Related to the above question, MagicLens presumably has a larger drop because of *shorter form sentences in its training*.\n\n**Relational.** To give every text query an explicit link to its paired reference image, we rewrite all text queries by prepending a short relational clause while keeping the original order and casing intact. Example: “with fog” becomes “the same object instance depicted in the image but with fog”. This yields a suite of explicitly relational captions that stress compositional understanding without altering the original intent. The ChatGPT prompt used to convert the text queries is: \n\n```Rewrite every text query so that it explicitly refers to the same object instance of the image query, while preserving original words in the same order. Simply prepend a concise relational clause, avoid introducing new concepts or proper names, and return each pair as ‘original query’ : ‘relational query’, with no additional commentary. Example: ‘with fog’ becomes ‘the same object instance depicted in the image but with fog’```\n\nWe observe that the drop of BASIC is noticeably smaller than that of FreeDom, but also *larger than that of MagicLens*. This pronounces the benefit of training for composed image retrieval, and a drawback of training-free methods, which is due to the fact that there is no or little relational information during the CLIP pre-training. Nevertheless, BASIC still performs reasonably well.\n\nInterestingly, evaluating using the 10 rewritten queries of CIRR (shown in the Table below) to make them non-relational (concise) demonstrates a *large performance increase* for BASIC from 18% mAP (10% R@1) to 44.4% mAP (40% R@1). This signifies that the relational description might not be the best way to describe those queries either.\n\n| Text-query Variant | mAP | R@1 | R@5 | R@10 | R@50 |\n|--------------------|----:|----:|----:|-----:|-----:|\n| Relational | 18.0 | 10.0 | 40.0 | 60.0 | 80.0 |\n| Concise    | 44.4 | 40.0 | 60.0 | 70.0 | 90.0 |\n\n| Original relational caption of CIRR | Concise, absolute rewrite of caption |\n|-----------------------------|---------------------------|\n| “Make the dog older and have two birds next to him and make everything look like a painting.” | as a painting of an older dog with two birds |\n| “Change the background to trees and remove all but one dog sitting in grass looking right.” | as a single dog sitting in grass with trees in the background |\n| “Remove the mannequin and change the necklace to a bracelet.” | as a standalone bracelet without a mannequin |\n\n**Instructional.** We recast every text query as an *imperative* instruction that tells the system what to do with the reference-image object. Concretely, we prepend an action verb (e.g. “turn”, “place”, “render”) and then a clause like “the object depicted in the image”, then append the original words in the original order. Example: “in a live action scene” becomes “adapt the object depicted in the image into a live action scene”. The ChatGPT prompt used was: \n\n```Rewrite each text query as a concise imperative instruction that starts with an appropriate action verb, explicitly mentions something like ‘the object depicted in the image’, preserves the original words in the same order, introduces no new concepts, and output each pair exactly as ‘original query’ : ‘instructional query’ with no commentary.```\n\nThe performance pattern across the methods in this case mirrors the relational case.\n\n---\n\n### Newer Baselines\n\nThe experiments already include comparisons against methods from 2024-2025. Specifically, FreeDom is a WACV 2025 oral, MagicLens is an ICML 2024 oral, CIReVL (supplementary Table S2) is an ICLR 2024 poster publication. Following the suggestion of Reviewer rzCM, we have now included **CoVR-2** (TPAMI 2024) and **MCL** (ICML 2024). BASIC consistently outperforms both methods across 5 CIR datasets.\n\n---\n\n### Robustness of Projection &amp; Fusion Corpora\n\nAs described in supplementary § S2, we use ChatGPT to construct a generic object-centric corpus (C_{+}) that defines the projection subspace. The corresponding stylistic/contextual corpus (C_{-}) is detailed in our response to Reviewer przL. These corpora are fixed and shared across all datasets and domains, enabling training-free and scalable retrieval without task-specific tuning. To assess robustness, Tables S5 and S6 of the supplementary report results using domain-specific corpora (e.g. mobility-, landmark-, or fashion-focused). These dedicated variants yield only a +0.4 mAP improvement on average, indicating that the generic corpus is already effective across diverse datasets. At the same time, the projection matrix can be recomputed from any text pool in *under 1 second on CPU*, making the method trivially adaptable at deployment time if domain-specific knowledge is available. A natural direction for future work is to automatically construct such corpora by parsing the dataset or its metadata using a large vision-language model (LVLM), which could eliminate the need for manual corpus design entirely."}, {"review_id": "aN3TLXk3ah", "reviewer": "Reviewer_FuSR", "summary": "This paper focuses on composed image retrieval (CIR), where given an image and a textual condition, images that fulfill both have to be retrieved. Authors highlight that there are no high-quality datasets to train and evaluate CIR systems, so they propose their own evaluation dataset called i-CIR. To build the dataset, they use a semi-automatic approach, combining Vision-Language Models for image collection and manual annotation. The dataset contains various types of hard-negative images which makes the retrieval challenging. Afterwards, authors also propose their own CIR system, called BASIC, avoiding any training stage (hence they use the term of zero-shot CIR). BASIC is a pipeline systems that leverages dual-encoder VLMs such as CLIP. It has various modules, namely i) centering for bias removal, ii) projection onto semantic subspace, iii) image query expansion, iv) contextualization of text queries, and v) score normalization and fusion. BASIC is evaluated on i-CIR; but also on some other reference datasets for CIR, such as ImageNet-R, NICO++, MiniDN and LTLL. Results show the overall superior performance of BASIC compared to several baselines and SOTA approaches. Finally, authors also run an ablation study measuring the contribution of each of the components of their pipeline for BASIC.", "questions": "1. Suggestion: this is entirely in authors' hands, but I would recommend to change the title of the paper and write something more specific. \"Instance-Level Composed Image Retrieval\" is too generic in my opinion.\n2. Question: in Line 52, authors say that \"The image-to-image similarity is enhanced through a projection learned not in the image space, but...\". Isn't this a contradiction with the claim that BASIC is a training-free approach? In Section 4, I didn't see that the projection is learned. \n3. Related Work: this section is very short and lacks a critical view of the literature. Authors should position BASIC in the \"Methods\" paragraph, highlighting the limitations of current methods and where BASIC makes a contribution. Similarly, in the \"Datasets\" paragraph, authors should discuss how i-CIR is different from existing approaches. I think this is already done in Section 3.4, so I would move that information to Related Work and that way, Section 3 would automatically be motivated.\n4. Question: in Line 125, authors say \"The neighbors retrieved from LAION are classified as visual, textual and composed hard negatives\". How do you do that? Manually? You use a classifier? Please, specify.\n5. Question: in Line 128, we can read \"Annotators then manually inspect...\" How many annotators? How do you recruit them? Please, give details about the annotation process, which is very important.\n6. Question: Line 200: \"To ensure scalability and generalization, we compute $\\mu^v$  using a large external dataset $X^v$, e.g. LAION.\" As far as I understood, the images of i-CIR are also collected from LAION. Is this fair? Are not you using the statistics of the test dataset for the benefit of your system?\n7. Typo: Line 284: a) and (b) are used differently. Please, either use (a) and (b) or a) and b).\n8. Question: Line 289: \"The only exceptions are fashion, ... and art, ...\" Any explanation for this? I think it would be interesting to analyze some examples and try to understand what's going on those categories. It may show potential ways to improve BASIC.\n9. Suggestion: Table 2. During the text, authors use line numbers to refer to this table, but there are not line numbers in the table. Please, add line numbers to make the reading easier.", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths:**\n1. The i-CIR dataset is an important contribution for CIR research. The semi-automatic process designed for the dataset creation can be leveraged to update and extend the dataset in the future.\n2. The proposed BASIC system is also very interesting. It shows that pre-trained VLMs such as CLIP can provide very rich representations which can be enhanced and used for various scenarios without any further training. This is specially interesting for problems such as CIR, where high-quality annotated data is hard to obtain. \n3. The results obtained in various CIR datasets are promising for BASIC.\n\n**Weaknesses:**\n1. My main concern is about BASIC development methodology. BASIC has many hyperparameters that have to be tuned, as explained by authors in Section 4. However, authors do not detail how they set the values of those hyperparameters. Indeed, in the supplementary materials, Section S3.1, authors state (caption of Figure S3): \"Effect of key hyperparameters on mAP (%) across all considered datasets\". Should we understand that the hyperparameter search is performed on the test datasets? That is my conclusion, since I cannot find any development dataset in the paper (nor in the supplementary materials). Furthermore, the ablation studies provided in Section 5.3 are also performed on the test datasets. This means that authors could test and select the components of BASIC directly on the test sets and use the components that maximize the performance. Those two considerations make me suspect that authors have developed BASIC on the test dataset directly, which is not methodologically correct, since they are \"optimizing\" their system for the test datasets. My score to the paper has been heavily influenced by those methodological flaws, but if authors can explain and correct those important issues, I would happily increase my score.\n2. The other concerns I have are lighter. I will list them as questions and suggestions for the authors.", "was_revised": true, "final_justification": "My major concern, how the hyperparameters of their system were tuned, has been suitably addressed by the authors, so I think that the paper should be accepted. The paper has many strong points in my opinion, as I listed in my review.", "rebuttal": "We thank **Reviewer FuSR** for emphasising the key strengths of our work:  \n(i) a **carefully engineered, extensible i-CIR benchmark**,  \n(ii) a **genuinely training-free** BASIC pipeline that **unlocks pre-trained VLMs for CIR**, and  \n(iii) **consistently promising results across five datasets**.  \nBelow we clarify the remaining concerns—most notably hyper-parameter (HP) selection—and address every question in turn.\n\n---\n\n### Hyper-parameter Tuning is *Never* Done on the Test Sets\n\nWe *apologize for any confusion* and we would like to *thank the reviewer for this comment*. We have omitted some information in the original submission, which we provide herein. During the development of the BASIC method, we reserved a small slice of the i-CIR crawl as a *development* set; none of those images appear in the final test benchmark. i-CIR_{dev} consists of 15 object instances, 92 composed queries, and 45K images in total. All BASIC HPs—contrastive scaling α, number of PCA components k, and Harris regularization λ—were fixed on this split and then frozen. The purpose of Fig. S3 is to show that BASIC’s performance curve is stable over a reasonably wide neighbourhood of hyperparameters (HP). To further demonstrate this robustness, we now perform a cross-dataset check. We set each dataset in turn as the development set and evaluate on the others in a “leave-one-out” logic.  \nEven though the largest performance appears on the diagonal, there is no large difference compared to tuning on the development dataset (or any other dataset). A small exception is LTLL, presumably due to its limited unique textual queries (2) and its very small size.  \nWe shall definitely *add this leave-one-out table* in the supplementary material of the paper. We shall also explicitly mention the development set that was used for tuning BASIC's HP.\n\n| Dev → Test (HPs) | i-CIR | ImageNet-R | NICO++ | MiniDN | LTLL | Avg. |\n|------------------|------:|-----------:|-------:|-------:|-----:|-----:|\n| i-CIR (α=0.2,k=300,λ=0.1)      | **47.41** | 31.43 | 31.06 | 38.89 | 41.90 | 38.14 |\n| ImageNet-R (α=0.4,k=200,λ=0.1) | 46.34 | **32.63** | 31.39 | 38.49 | 40.41 | 37.85 |\n| NICO++ (α=0.2,k=200,λ=0.1)     | 46.35 | 32.55 | **31.85** | 39.81 | 41.32 | 38.38 |\n| MiniDN (α=0.2,k=200,λ=0.1)     | 46.35 | 32.55 | 31.85 | **39.81** | 41.32 | 38.38 |\n| LTLL (α=0.0,k=300,λ=0.1)       | 46.22 | 28.96 | 30.16 | 37.65 | **42.44** | 37.09 |\n| i-CIR_{dev} (α=0.2,k=250,λ=0.1) | 47.39 | 32.13 | 31.65 | 39.58 | 41.38 | 38.42 |\n\n---\n\n### ``Learned projection'' vs Training-Free\nWe use the term “learned” as commonly used for PCA which is data-dependent.  \nTherefore, the projection in BASIC is not learned via supervision or optimization, but rather *computed analytically* from two fixed text corpora:  (C_+) (object-centric terms) and (C_-) (style/context terms). As described in section 4 (Eq. 2), we construct a contrastive covariance matrix and perform eigendecomposition to extract the top positive eigenvectors, which define the projection space. This process involves **no training, backpropagation, or labeled data**, and is consistent with BASIC’s training-free design. To avoid misinterpretation, we will revise the sentence.\n\n---\n\n### LAION Statistics Do Not Leak Test Information\n\nLAION is very large; i-CIR overlaps with it by <0.001 %. Moreover, each instance of i-CIR has its own tailored database consisting of the explicitly curated hard negatives. Using the global CLIP LAION mean therefore does not leak any test set statistics, yet gives a domain-agnostic center that transfers well across all datasets. To substantiate this, we computed the center on ImageNet-1K and simply replaced the LAION mean with this new one. BASIC on i-CIR achieves 46.67% mAP with the ImageNet mean versus 47.39% mAP with the LAION mean, a negligible drop of \\<0.8 mAP. This small gap confirms that our results are not artefacts of test leakage, but are the result of the BASIC design.\n\n---\n\n### Details of i-CIR Construction\n\nThe labelling of the neighbours retrieved from LAION is a *pseudo-labelling*. For example, if the seed sentence is a textual description of the instance like “Temple of Poseidon”, then the retrieved neighbours should depict exactly this instance OR in a worse case just “temples”. Thus, these images are pseudo-labeled/classified as visual hard negatives. This is what L126 “for cases (i \\& ii), (ii), and (iv \\& v), respectively” was trying to describe. We shall rephrase for more clarity. The annotators were trained workers performing a well-defined annotation task. They completed a one-week training on composed image retrieval task, as also on copyright and sensitive-content removal. We shall include these details in the supplementary material. Thanks for the suggestion. \n\n---\n\n### Why BASIC Trails in the Fashion Category\n\nMagicLens is pre-trained on 36.7 M triplets dominated by words such as “brand” and “style” (according to their Fig. 5 b), which explains its narrow advantage in fashion.  Outside that niche, BASIC leads by up to 34.8 % mAP (household category). We will add exemplar failure cases per visual category in the supplementary material.\n\n---\n\n### Other Questions & Promised Edits\n\nWe will consider a more specific variant of the title. We will move some info from the i-CIR comparison (current section 3.4) into section 2 and add some comments positioning BASIC among composed image retrieval methods. We will correct any typos."}]

Author rebuttal:

Meta-review: Key contributions:

i-CIR benchmark: curated visual/text/composed hard negatives; substantially harder than legacy sets.
BASIC method: strong zero-shot results across i-CIR, ImageNet-R, NICO++, MiniDN, LTLL.

2) Main weaknesses: Whether mitigated

Availability/reproducibility at review time: Dataset/code not public; missing significance tests/compute details. Somewhat mitigated during discussion.

Some discussion about human-subjects compliance for annotations.

3) Most important reasons for accept recommendation: Reviewers (nearly) universally positive.  Most negative reviewer was imposing an implicit requirement that dataset needs to be public at time of submission.  This is not the NeurIPS policy and I believe that the authors fulfilled community expectations as they exist today.
Decision: Accept (poster)