Seeing Through Words: Controlling Visual Retrieval Quality with Language Models
Authors: Jianglin Lu
Simon Jenni
Kushal Kafle
Jing Shi
Handong Zhao
Yun Fu
Venue: ICLR 2026 Poster (2026)
Keywords: Large Language Models
Vision-Language Models
Query Completion
Retrieval
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Text-to-image retrieval is a fundamental task in vision-language learning, yet in real-world scenarios it is often challenged by short and underspecified user queries. Such queries are typically only one or two words long, rendering them semantically ambiguous, prone to collisions across diverse visual interpretations, and lacking explicit control over the quality of retrieved images. To address these issues, we propose a new paradigm of quality-controllable retrieval, which enriches short queries with contextual details while incorporating explicit notions of image quality. Our key idea is to leverage a generative language model as a query completion function, extending underspecified queries into descriptive forms that capture fine-grained visual attributes such as pose, scene, and aesthetics. We introduce a general framework that conditions query completion on discretized quality levels, derived from relevance and aesthetic scoring models, so that query enrichment is not only semantically meaningful but also quality-aware. The resulting system provides three key advantages: 1) flexibility, it is compatible with any pretrained vision-language model (VLMs) without modification; 2) transparency, enriched queries are explicitly interpretable by users; and 3) controllability, enabling retrieval results to be steered toward user-preferred quality levels. Extensive experiments demonstrate that our proposed approach significantly improves retrieval results and provides effective quality control, bridging the gap between the expressive capacity of modern VLMs and the underspecified nature of short user queries. Our code is available at https://github.com/Jianglin954/QCQC.

Published as a conference paper at ICLR 2026
SEEING THROUGH WORDS: CONTROLLING VISUAL RETRIEVAL QUALITY WITH LANGUAGE MODELS
Jianglin Lu Simon Jenni'
Kushal Kafle
Jing Shi"
Handong Zhao' Yun Fu²
1 Adobe Research "Northeastern University
ABSTRACT
Text-to-image retrieval is a fundamental task in vision-language learning, yet in real-world scenarios is often challenged by short and underspecified user queries. Such queries are typically only one two words long, rendering them semantically ambiguous, prone to collisions across diverse visual interpretations, and lacking explicit control over the quality of retrieved images. To address these issues, we propose a new paradigm of quality-controlle controllable retrieval, which en- riches short queries with contextual details while incorporating explicit notions of image quality. Our key idea is to leverage a generative language model as a query completion function, extending underspecified queries into descriptive forms that capture fine-grained visual attributes such as pose, scene, and aesthetics. We intro- duce general framework that conditions query completion on discretized quality levels, derived from relevance and aesthetic scoring models, so that query enrich- ment not only semantically meaningful but also quality-aware. The resulting system provides three key advantages: @ flexibility, it is compatible with any pretrained vision-language model (VI.Ms) without modification; @ Iransparency, enriched queries explicitly interpretable by users; and @ controllability, en- abling retricval results to be steered toward iser-preferred quality levels. Exten- sive experiments demonstrate that proposed approach significantly improves retrieval results and provides effective quality control, bridging the gap between the expressive capacity of modern VLMs and the underspecified nature of short user queries. Our code is available at http::/githubb.commaaagggnnnonit
INTRODUCTION
Text-to-image retrieval (T2IR) aims to return the most relevant images from a gallery given a textual query. Recent progress in this task has been largely driven by vision-language models (VLMs) (Jia et al., 2021; Yang et al., 2022; Li et al., 2023; Yang et al., 2025; Lu et al., 2025a; Huang et al., 2025), which learn joint representations of text and images through large-scale pretraining on web-scale image-text pairs (Schuhmann et al., 2021; 2022; Liu et al., 2023). These models significantly nar- row the semantic gap between modalities and achieve strong alignment across diverse benchmarks (Ilharco et al., 2021; Singh et al., 2022; Li et al., 2024; Lu et al., 2025b; Dong et al., 2026).
Despite these advances, retrieval performance often degrades in realistic scenarios where user queries are very short (typically just one or two words, e.g., "a dog"). Short queries encode only limited semantics, which results in large and ambiguous search subspaces and less discriminative results. This issue becomes more pronounced in large-scale galleries, where underspecified queries yield many candidate matches and cause semantic collisions among visually diverse results.
Another limitation of existing retrieval systems is their singular focus semantic alignment. Naive retrieval approaches simply return the top-k images with the highest similarity scores, overlooking other critical aspects of user satisfaction such aesthetics, interestingness, or popularity (Yi et al., 2023; Abdullahu & Grabner, 2024; Wang et al., 2025). In practice, retrieval quality is context- dependent: art students may prefer visually inspiring images, architects may seek unique and cre- ative references, and shoppers may favor popular or visually appealing products. However, conven- tional retrieval systems lack mechanisms for steering retrieval toward these quality dimensions.
"Corresponding author; jianglinlu@outllnn wow.oookou...oooomoooooo Work done during an intersship Adohe Research.

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Published as a conference paper at ICLR 2026
To address these limitations, introduce the task of quality-controllable retrieval (QCR). For- mally, given a frozen VLM and a short textual query, the objective is to retrieve images that not only align semantically but also satisfy user-specified quality requirements. This setting is feasible because short queries occupy a broad region of the embedding space that contains images of varying perceptual quality. With appropriate conditioning, this region can be partitioned into perceptually distinct subsets, enabling fine-grained quality-aware retrieval.
In this work, we define retrieval quality along two widely applicable dimensions: relevance (seman- tic consistency) (Cherti et al., 2023) and aesthetics (visual appeal) (Yi et al., 2023). For each image in the gallery, we construct auxiliary annotations consisting of textual description, a relevance score, and an aesthetic score. We discretize these continuous scores into categorical quality levels and associate each description with its corresponding quality condition.
The central challenge is how to steer retrieval results toward specific quality levels given short queries. We propose a simple yet effective solution: qualiny-conditione query completion (QCQC). QCQC enriches short queries with quality-aware details by leveraging generative large language model (LLM). Trained on the quality-augmented dataset, the LLM learns to append appropriate descriptive phrases that capture both semantic and quality-related attributes. By conditioning on distinct quality levels, QCQC generates targeted query completions that steer retrieval toward spe- cific regions of the embedding space. This capability is particularly valuable in practice, users often struggle to articulate quality preferences or may lack a clear understanding of what constitutes "high" "low"" quality within dataset. By modeling how textual descriptions vary across quality levels, our approach bridges this gap and enables more controllable, quality-aware retrieval through conditioned query completion. Our key contributions in this work can be summarized as follows:
A new problem: we introduce quality-cnttrollabee retrieval, new setting where retrieval can be explicitly conditioned on user-defined quality requirements.
A general solution: we propose QCQC, generative query completion framework that leverages LLMs to enrich short queries with quality-aware descriptive details.
Validation: we conduct extensive experiments to show that QCQC effectively steers re- trieval outcomes according to quality preferences and is compatible to multiple VLMs.
PRELIMINARIES
2.1 MOTIVATION
We study the problem of text-to-image retrieval, where the goal is to return the desired images from a large gallery given a set of natural language queries. Specifically, let Q == (Qi..... Qm) denote collection of m text queries and I := {.I..... 1n} } image gallery of size We consider -fate-of-the-art VLM as the retrieval backbone, equipped with a text encoder g Q - Rd and an image encoder f : I + Rdd both producing d-dimensional normalized embeddings. Given query set Q, the system returns the top-1) relevant images according to
X == sort (J(I), g(2), 1)),
where X C denotes the top-n matches of queries Q. The sort function typically operates on the similarity scores S E mR with Sij == ,(Q.) f(z)).
Although modern VLMs achieve strong cross- modal alignment, retrieval performance deteriorates in realistic scenarios where user queries are usually very short (typically just one or two words, e.g., "a dog"). Given such short queries, naive retrieval system faces several challenges: ① Semantic ambignity: a few words can refer to wide range of possible images, Icading to a large and diffuse search subspace with less discriminative retrieval results. @ Semantic collisions: short queries tend to yield close similarity scores for visually diverse images. These collisions confuse ranking and are particularly problematic in large-scale galleries where many candidate images match the vague query. & Lack of quality control: the quality of retrieved images is not explicitly enforced during retrieval. At best, one can apply rest-vetrieval filtering, but the system itself provides no mechanism to ensure that high-quality results consistently appear among the top matches. These issues highlight a fundamental gap between the expressive capacity of modern VLMs and the underspecified nature of user queries, motivating the need for query enrichment and controllable retrieval mechanisms.
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2.2 PROBLEM SETTING
To address the above limitations, we propose to enrich short queries with additional descriptive details that potentially capture more distinguishable attributes of images. Formally, let h denote query completion function that maps Q to enriched queries l()). Retrieval is then performed as
X = sort (S(I), ((((2)), n))
where h(Q) augments the short queries with contextual details. The enriched queries are expected to capture not only object categories but also additional information such as pose, scene, action, and fine-grained attributes. To be effective, the completion function should be aware of the retrieval gallery, so that generates meaningful context rather than irrelevant content.
To achieve this, implement h using generative large language model (LLM). However, simply training the LLM on image descriptions is insufficient, since cannot guarantee that retrieval results satisfy user expectations of quality. Instead, we partition the textual descriptions into non-overlapped quality levels C' that reflect different image quality categories. We then finetune the LLM with these quality levels, enabling it to generate query completions conditioned on quality preferences. This yields the formulation of our quality-comtrollable retrieval (QCR):
X sort (J(I), ((LLM( C)), 1))
where LLM( Q; C) expands the short queries based on the specified quality constraint C. The extended queries thus steer retrieval toward images that align with the desired quality criteria.
This approach offers several practical benefits: ① Flexibility: it requires modification to pre- trained VLMs and remains compatible with any VLMs, @ Transparency; the generated query com- pletions are human-readable, allowing users review and select preferred options. @ Controlla- bility: the LLM can produce different query completions according to distinct quality conditions C, enabling explicit quality control during retrieval. In the following section, we provide theoretical justification for why enriching short queries may improve retrieval performance.
2.3 THEORETICAL ANALYSIS
We model query completion as a structured perturbation and analyze its effect on the similarity matrices S through the lens of rank variation under perturbations.
Let Q+ = -. ) = h(Q) denote the extended queries by h. where Q = Q, + suffix;, Vi (1. m). and suffix, denotes additional descriptive details appended to query Qi- Let C E gaxd be the image embedding matrix with rows сj == f(4,) E IR, Vj E [I....,n), and A, B E Rmxd be two sets of text embeddings with strict one-to-one pairing of rows, with rows a; == g(Qi) E Rd and b, == s(QT) Rd, Vi E (1....,m). Let == rank(A) be the rank of A, (A) be the smallest nonzero singular value of A, and A = UEV denote its sin- gular value decomposition (SVD). We then partition the right singular vectors as V Vs V1] where Vs E dd and V E (d-(r)) satisfy span Vs) R(A) and span (V R(A)* with R(A) = (a
a C Rdd the row space of A.
Definition We define a perturbation matrix A ==
A E R.xdd score matrices S^ = ACT E pm and SB = BCT pu for the queries Q and Q+, As AVs, As == A Vs.
A = AV1. Cs = CVs. C1 == CV1, X == (As CS) Y AIC 1, := coll X),
and P Px as the orthogonal projector onto U.
Lemma 1. If raan(XX) = r and | P Y, Yr112 < 1, then rank (X, -++P =T
Lank(X;+3)
Proposition Assume that: i) || 112 or (A); ii) there exists 1 S (L...., n) with |4| r such ||
that the columns X, form a basis Uf; ii) X PY1l12 < 1; and iv) there exists disjoint index set ) \ such that = rank (I (I-P2a 22 2 1, where == (I PI- Z, := Z ZK := Z.K, and Pz, be the orthogonal projector onto col (z). Then, rank(SB) > rank
K C (1,...,,1 n)
This proposition indicates that, under certain conditions, the score matrix So derived from query completion can express more independent scoring patterns and has the ability to potentially make finer-grained distinctions. The detailed proof and analysis are provided in the Appendix A.1.
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Los Modiun
Ice
1204
20
30
204
9114
7X55
8807
Picc
(a) Acs: [2.115, 7-508]
(b) Rel: [0.252, 0.562]
(c) Acs: [2.782, 6.961]
(d) Rel: [0.201, 0.596]
Figure 1: Aesthetic and relevance score distributions of F clickr2.... 4M in (a) and (b), and of MS-COCO in (c) and (d). It worth noting that the numbers of high-quality and low-quality images
are limited, which leads to the average scores of any two random sets being very close.
QUALITY-CONDITIONE QUERY COMPLETION
3.1 QUALITY DEFINITION
For the proposed QCR task, we require a clear notion of quality. In this work, we characterize quality along two primary dimensions: ! Relevance, which measures the semantic consistency between textual queries and their corresponding images; and @ Aesthetics, which reflects the visual appeal or attractiveness of retrieved images. Note that the proposed QCQC framework is inherently flexible, permitting the incorporation of arbitrary quality metric, provided that reliable scoring models are available and applicable to general image datasets. Other notions of quality, such as interestingness (Gygli et al., 2013; Abdullahu & Grabner, 2024) can also be adopted in similar manner and are left for future exploration. To facilitate user control over retrieved results, we discretize the quality dimensions into non-overlapping conditions. Specifically, we define C for relevance and CAA for aesthetics, each partitioned into perceptually distinct and user-friendly levels. For example, both can be represented as CR CA [Low, Medi um, High.
3.2 DATA GENERATION
To ensure that the completion function can perceive the retrieval gallery, we construct an augmented training dataset for each gallery I. The dataset integrates three complementary components: textual descriptions T {T:) of images, relevance scores s" E R,"", and aesthetic scores s" E R".
Textual Descriptions. For each image It, we generate textual description T; using an image caption model CAP(-), i.e., T; = CAP(L,, Vi E [1.....n), In our experiments, we utilize strong pretrained captioning models without additional fine-tuning for description generation. Each T, is concise sentence summarizing the main content of the image.
Aesthetic Scores. We assign an aesthetic score 8 to each image 4; using an aesthetic evaluation model EV a-), i.e., S. EV (I:). Vi E (1..... n2.. The aesthetic scores represent the visual quality of the images, with higher scores indicating greater visual appeal.
Relevance Scores. For each deser-etciiition pair {4,Ti}. we compute a relevance score using a pretrained VL.M. Specifically, we extract the image feature fT) and text feature g(T.), then caleu- late their cosine similarity as their relevance score, i.e., s = cos (os( Vi E {1,.... n).
3.3 TRAINING FRAMEWORK
Score Discretization. To simulate quality-controlled retrieval, we discretize the continuous quality scores of images into categorical levels that are more intuitive for users. Given score vector (cither aesthetics 8" or relevance 8*), each score r; is mapped into one of three descriptive levels by partitioning the score distribution into three percentiles:!:
Low,
r; pera(r, P1),
1(r.)=
High, Medium,
r; > perc(r, p2),
(4)
otherwise.
!Our framework is general and supports arbitrary numbers of levels depending on the desired granularity. In Table 5, Sec. 4.5, we provide example with five quality levels.
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Table 1: Query completions with their retrieved images and quality scores on COCO
Rel: Low, Aes: Low
Rel: Medium, Aes: Medium
Rel: High, Aes: High
train that is sitting the train sitting the tracks with tracks in gravel Aes 4.715, Rel 0.347
train is traveling some water and houses Aes 5.935, Rel 0.394
black smoke coming out of it Aes 4.818, Rel 0.382
bird standing the ground near some leaves Aes 4.616, Rel 0.346
bird flying above some brown water Aes 5.079, Rel 0.374
bird flying across water at the beach
Aes 5.120, Rel 0.386
a teddy bear wearing eye glasses and laying bed Aes 4.788, Rel 0.359
a teddy bear that is sitting on tree Aes 5.649, Rel 0.388
a teddy bear sitting on a wall next to old stone house Aes 5.818, Rel 0.437
Here, is the score of the i-th sample and perc(r, .)) calculates the p% percentile of r as
perc(r, p) F[LEJ] (5 [EJ) (FILE] + 1] -l1..)).
(5)
p (n- - 1), F is the sorted version of and |-| is the floor function. Figure illustrates where ç = 100 distributions aesthetics and relevance scores and their discretized partitions.
Instruction Design. We then train the completion function LLM on the augmented training set
D = (T, 80 ;²)) The discretized quality levels serve as explicit conditions within instructions,
enabling the LLM to generate quality-aware query completions. For each image 4,, we design concise instruction P. of the following form:
"Relevance: I(s)), Aesthetic: ((s?), Query:
where ((s{)) and ((sf)) represent the categorical quality levels defined in Eq. (4). During training, this instruction provides a lightweight yet effective mechanism to condition query generation on the specified quality preferences.
Model Training. To stimulate the quality control process during model training. we use the de- scriptive levels ((s{) and ((s?) of image I; as the quality conditions, which are incorporated into the instruction P. We then concatenate instructions P. with the textual description T; for each image J, and then train the proposed QCQC model with the standard autoregressive next-token predic-

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Table 2: Query completions with their retrieved images and quality scores on Flickr2,
Aes: Low, Rel: Low
Aes: Medium,
Rel: Medium
Aes: High, Rel: High
chair with red and black ropes on Aes 4.847, Rel 0.379
a chair with wires on it Aes 4.019, Rel 0.362
a chair on a stage a field
Aes 5.257, Rel 0.387
a cell phone on tripod in front of waterfall in yellow- stone national park Aes 5.441, Rel 0.429
cell phone with wires at- tached to it
cell phone with an acous- tic guitar on it
Aes 4.531, Rel 0.362
Aes 5.035, Rel 0.390
bowl of soup with meat and vegetables in it Aes 4.648, Rel 0.343
bowl on display Aes 4.980, Rel 0.379
a bowl with flowers on it
Aes 5.386, Rel 0.387
tion loss. In this way, our QCQC model learns to generate query completions that are not only semantically relevant but also controllable according the given quality constraints.
Inference Strategy. During the inference stage, we concatenate a similar instruction with each test- ing query. To simulate user preferences, evaluate various relevance-aesthetic combinations, such as "low relevance, low aesthetic" and "high relevance, high aesthetic". Then the model generates completed queries based on the instructions, testing queries, and specified quality conditions. For efficient similarity search on large-scale galleries, we utilize the FAISS library (Johnson et al., 2019) identify the nearest images for the queries.
EXPERIMENTS
4.1 EXPERIMENTAL SETTINGS
Datasets. We evaluate our method on two image datasets: one with real textual descriptions and without. For the image-only one, construct large dataset sourced from the Openverse website (Openverse, 2025). We refer to this dataset as Flickr2. 4M, which contains over 2.4 million CCO-licensed images randomly selected from the Flickr subset of Openverse. For image

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Table Retrieval quality of various methods on Flickr2. 4M. CoCa and Blip2 are used to gener- ate textual descriptions; L (Low), M (Medium), and H (High) indicate the quality conditions; and Ctrl specifies whether the method enables controllable retrieval over quality. For both average relevance (Ave Rel) and average aesthetics (Ave Aes), higher values indicate better retrieval quality.
Quality
VLM
Aes Cond Rel Cond
M
H
Ctrl ?
M
H
Ave Aes Ave Rel
4.735
4.735
4.735
4.735
4.735
4.735
4.735
4.735
4.735
Prefix
0,350
0.350
0.350
0.350
0.350
0.350
0.350
0.350
0.350
x
Ave Acs
4.730
4.822
4.831
4.823
4.837
4.784
4.798
4.722
4.842
LLaMA3
Ave Rel
0,351
0,351
0.351
0.353
0.351
0.350
0.354
0.354
0,352
x
Ave Aes
4.359
4.651
4.728
4.712
4.668
4.791
4.791
4.816
5.056
GPT-4o
Ave Rel
0.378
0,361
0.357
0.358
0.360
0.356
0.361
0.357
0,361
x
Ave Aes
4.776
4.556
4.722
4.811
4.781
4.757
4.693
4.751
4.746
PT
Ave Rel
0.345
0.346
0.349
0.349
0.346
0.348
0.345
0.350
0.350
x
Ave Aes Ave Rel
4.756
4.834
4.777
4.838
4.863
4.882
4.821
4.905
4.770
FT
CoCa
0.365
0.368
0.364
0.363
0.369
0.368
0.365
0.364
0.365
x
Ave Acs
4.458
4.615
4.530
4.934
4.852
4.841
5.222
5.170
5.270
Ours
CoCa
Ave Rel
0,355
0.366
0.391
0.354
0.360
0,386
0,353
0,368
0,390
Ave Acs
4.795
4.871
4.890
4.894
4.844
4.856
4.901
4.847
4.888
FT
Blip2
Ave Rel
0,370
0.368
0.367
0.367
0.371
0,366
0,367
0.371
0,369
x
Ours
Blip2
Ave Aes
4.541
4.523
4.455
4,940
4.906
4.922
5.309
5.222
5.191
/
Ave Rel
0.353
0.370
0.397
0.354
0.366
0.396
0.355
0.372
0.390
datasets with real textual descriptions, we adopt the widely-used MS-COCO dataset for experiments, which includes both images and human-annotated descriptions. Specifically, we utilize the training subset of MS-COCO, which consists of 118, 287 samples, each sample containing one image and five corresponding descriptions. In total, approximately 0.6 million descriptions are used for training.
Model Selection. For the backbone of our method, we evaluate two different LLMs: GPT2-1.5B (Radford et al., 2019) and Qwen2.5-0..5B (Yang et al., 2025). Other L.LSs can be validated similarly and we leave them for future study. We adopt two caption models CAP(-), including pretrained CoCa (Yu et al., 2022) and pretrained Blip2 (Li et al., 2023) model. For feature extraction, we adopt a pretrained VLM OpenCLIP
(Cherti et al., 2023; IIharco et al., 2021). The relevance score is computed as the cosine similarity between the features of each image-description pair. For EV a-), we use a pretrained aesthetic predictor (Schuhmann, 2022).
HITT-H-14-quicgeer
Implementation Details. For GPT2-1.5B (Radford et al., 2019), set the learning rate, warmup steps, number of epochs, and batch size to 2e 3, 100, 50, 150, respectively. For Qwen2.5-0.5B (Yang al., 2025), these hyperparameters are set to 2c 5, 100, 30, and 80, respectively. For score discretization, we set P1 = 33 and p2 = 66 to divide the score distribution into three evenly spaced percentiles (examples of five-level cases are also considered). Note that we only train the query completion model LLM, while the quality evaluation model EV (-). (-), the caption models CAP(-), and the retrieval model VLM are all pretrained without additional fine-tuning. Since the pretrained caption models may occasionally generate non- -nnglisl characters, we clean these characters directly before training prevent potential issues for query completion. Before training, we prepend start token to the instructions and append an end token to the descriptions. The training loss is computed only on the tokens of the descriptions and the end tokens, while excluding those of the instructions.
Evalution Strategy. For performance evaluation, we use the 80 class names from MS-COCO dataset as query objectives. These include common objects such as trains, cars, and animals, as well as more specific categories like teddy bear, fire hydrant, and toothbrush. Based on the capitalization of each class name, we prepend either "a" or "an" to form the input queries. Since we focus on controlling the quality of retrieved images, calculate and report the average aesthetic and relevance scores of the retrieved images across all input queries as the final evaluation performance.
aesthetic and relevance scores the evaluation metrics. We
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Table 4: Retrieval quality of various methods on MS-COCO, where L (Low), M (Medium), and (High) indicate the quality conditions for retrieval, and Ctrl specifies whether the method enables controllable retrieval over image quality. For both average relevance (Ave Rel) and average aesthet- ics (Ave Aes), higher values indicate better retrieval quality.
Quality
Aes Cond
L
M
H
Ctrl ?
Rel Cond
L
H
M
L
H
Ave Aes
4.817
4.817
4.817
4.817
4.817
4.817
4.817
4.817
4.817
Prefix
Ave Rel
0.349
0.349
0.349
0.349
0.349
0.349
0.349
0.349
0.349
x
Ave Aes
4,903
4.891
4.855
4.916
4.875
4.880
4.871
4.858
4.911
LLaMA3
Ave Rel
0.348
0.349
0.347
0.348
0.349
0.347
0.348
0.350
0.344
x
Ave Aes
4.673
4.754
4.686
4.782
4.808
4.880
4.838
5.075
5.048
GPT-4o
Ave Rel
0.371
0.357
0.354
0.360
0.358
0.350
0.359
0.352
0.351
x
Ave Aes
4.819
4.793
4.789
4.829
4.810
4.828
4.794
4.826
4.820
PT
Ave Rel
0.343
0.340
0.344
0.348
0.339
0.344
0.346
0.343
0.340
x
Ave Aes
4.925
4.845
4.848
4.882
4,934
4.990
4.849
4.941
4.929
FT
Ave Rel
0.370
0.367
0.366
0.368
0.368
0.365
0.371
0.371
0.367
X
Ave Aes
4.878
4.852
4.859
4,902
4.858
4.941
4.952
4.961
4.944
FT-CoCa
Ave Rel
0.346
0.351
0,356
0.349
0,350
0.354
0.345
0.352
0.352
x
Ave Aes
4.828
4.815
4.785
4,932
4.894
4.893
5.034
4.948
4.933
FT-Blip2
Ave Rel
0,350
0.352
0,356
0.344
0.351
0.353
0.345
0.351
0.347
x
Ours
Ave Aes
4.811
4.790
4.773
4.911
4.873
4.862
5.016
4.983
5,024
V
Ave Rel
0.356
0.370
0.382
0.354
0.370
0.387
0.352
0.365
0.387
4.2
QUALITATIVE VALIDATION
We first perform qualitative analysis to validate whether our approach effectively achieves quality control in retrieval. In Tables 1 and 2, we present three retrieved images per query, along with their corresponding completed queries and quality scores under three different quality conditions, includ- ing "low relevance, low aesthetic", "medium relevance, medium aesthetic", and "high relevance, high aesthetic". As shown, our method generates distinct query completions for different quality conditions. From left to right, the quality level improves, both aesthetic and relevance scores increase accordingly. This demonstrates that our proposed method effectively controls the quality of the retrieved images. We provide more qualitative results with failure cases in the Appendix A.5.
4.3 QUANTITATIVE VALIDATION
Since there no existing text-to-image retrieval methods that can be directly applicable to the proposed QCR task, we design the following baselines for quantitative comparison: a) Prefix: using original short queries directly without query completion; b) PT (Pretrained): using pretrained LLM for query completion without finetuning; c) FT (Finetuned): finetuning a pretrained LLM on textual descriptions while conditioning on randomly generated quality scores; and d) general- purpose LLMs, including the LLaMA-3 (LLaMA-3-BBIInttter (Grattafiori et al., 2024) and GPT- 40 (via official API) (OpenAI al., 2024).
Tables 3 and 4 report the retrieval performance of the baseline models and our proposed method with Qwen2.5 (Yang et al.. 2025) on the two datasets, respectively. From these tables, we make the following key observations. ① Prefix-only retrieval yields unsatisfactory quality performance, highlighting the necessity of query completion. @ Pretrained models (without finetuning) for query completion degrade retrieval quality, performing worse than using only the query prefix in most cases. This is because these pretrained models tend to generate irrelevant words, negatively im- pacting retrieval performance. & Finetuning on textual descriptions improves both relevance and aesthetics compared to prefix-only and pretrained models. However, models finetuned on randomly assigned scores fail to effectively control the quality of the retrieved images. @ Our method not only enhances retrieval under high-quality conditions but also excels in quality control, demonstrating strong adaptability regardless of whether it trained real or generated captions.
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Table 5: Results with five quality levels.
Comparison with post-retrieval filtering, where the rerank method first retrieves the top-k im- ages based on relevance and then reorders the candi- dates by aesthetic scores identify the best result.
Table 6:
Relevance (Red + Red)
M
VL
L
VH
4.597
4.507
4.610
4.529
4.445
Aesthetics (Green + GGren)
VL
0.355
0.364
0.375
0.382
0.397
k:
3
5
10
4.805
4.765
4.825
4.729
4.761
Aes
4.735
4.947
5.014
5.198
5.313
L
Rerank
0.353
0.366
0.369
0,380
0.392
Rel
0.350
0.348
0.347
0.345
0.341
4,909
4.961
4.878
4.889
4.901
Aes
4.842
5.071
5.154
5.298
5.377
M
0.355
0.3642
0.370
0.3754
0.390
LLaMA3
Rel
0.352
0.349
0.347
0.342
0.337
5,028
4.967
5.045
4.952
5,009
Aes
5.056
5,205
5.293
5.393
5.518
H
GPT-4o
0.355
0.365
0.370
0.374
0.387
Rel
0.361
0.356
0.353
0.349
0.343
5.282
5.153
5.263
5.245
5.121
Aes
5.236
5,320
5.364
5,432
5,533
VH
0.355
0.363
0.371
0.378
0.389
Ours
Rel
0.387
0.385
0.381
0.376
0.366
4.4 DATASET DEPENDENCE
To achieve quality control in retrieval, the model should be tailored to the specific dataset, as dif- ferent datasets exhibit varying quality characteristics. To illustrate this, we conduct cross-dataset retrieval experiments. Specifically, we evaluate retrieval quality on MS-COCC using queries com- pleted by the model finetuned on F Flickr2. 4M. In Table 4, we assess FT-CoCa and FT-Blip2, which are finetuned on descriptions generated by CoCa and Blip2. respectively. The results indi- cate that both models achieve higher aesthetic scores quality conditions improve, suggesting that aesthetically relevant semantic cues may be universal across natural images. Nevertheless, they con- sistently exhibit low relevance across all quality conditions. This limitation stems from the dataset mismatch between the query completion and image retrieval stages, since the two datasets encode different semantic information. See Appendix A.3 for additional analysis and results.
4.5 FURTHER VALIDATION
Table 5 presents the results on the Flickr2.6 4M dataset across five quality levels: VL (Very-Low), (Low), M (Medium), H (High), and VH (Very-High). As shown, our method effectively enables fine-grained control over the quality of retrieved images, adhering to more nuanced descriptive con- straints. We also compare against a post-retrieval filtering baseline that first retrieves images based on relevance and then re-ranks the results by aesthetic scores. The comparison results listed Ta- ble 6. As shown, this two-stage strategy is unreliable for short queries, which typically offer vague representations and limited descriptive cues. As result, the initial retrieval set tends to be seman- tically broad and aesthetically subpar, leaving little room for the re-ranking step to improve. While increasing can surface images with higher aesthetic quality, it typically comes at the cost of re- duced semantic relevance, illustrating a trade-off between these two quality dimensions. In contrast, our method performs quality control during the query stage, which inherently guides retrieval to- ward the desired quality level. This quality-aware conditioning cannot be achieved by the two-step baseline, which lacks knowledge of the dataset's quality distribution and operates in a detached, post-hoc manner. See Appendix A.5 for more experimental results.
5 CONCLUSION
We presented a contrllle retrieval framework to address the limitations of short and under- specified text queries in text-to-image image retrieval. Our key idea is to enrich queries using generative language model conditioned on discretized quality levels, enabling retrieval that is both semantically expressive and aligned with user quality preferences. Extensive experiments demonstrate that our proposed QCQC approach effectively improves retrieval quality, serving as a flexible augmentation to existing VLMs while enabling quality control in retrieval. Future work will extend our method to other dimensions of quality beyond relevance and aesthetics, such as interestingness, diversity, or user personalization. We hope this work inspires further research on integrating controllable language- hased query enrichment with large-scale multimodal retrieval systems.
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STATEMENTS
ETHICS STATEMENT
This work investigates large language models for query completion in text-to-image retrieval, where image quality information is integrated into the training process. The study relies on publicly avail- able datasets and does not involve human subjects, private information, acknowledge that retrieval models may inherit biases present in the underlying vision-language datasets; however, approach does not introduce new data and instead focuses methodological contributions. The models and results are intended solely for academic research, and no harmful or deceptive applications pursued. We adhere the ICLR Code of Ethics and confirm that this research complies with principles of fairness, transparency, and responsible use.
sensitive content. We
REPRODUCIBILITY STATEMENT
We are committed to ensuring the reproducibility of our work. We have released our code at GitHub. Theoretical results stated with all necessary assumptions in the main text and the complete proofs are provided in the Appendix. Experimental settings are included in the main body. Together, these resources are intended enable full replication and verification of our results.
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A APPENDIX
A.1
PROOFS
Proof of Lemma I. Since col(X;) = U1 we have I, = X Hence
X + PY, = X, X X - X, +X
(6)
This factorization assumes PY, lies in col( 1). which true because P projects onto col(X) and col( (X) col(X,). Considering the Neumann Series argument: for any matrix E, if || 1, then (I + E) is invertible (i.e.. full rank). Given ||x] PYill2 (I, + PY,) is invertible. Thus, rank(X PY;) rank(X;) =r.
Proof of Proposition 1. Since right- muttipuuliiliiatioon by the orthogonal matrix [Vs V1 is rank- preserving, analyze the following matrices:
A' = AV A V [AVs AVI [As 0),
A' = AV A V1 [AVs AV1 = [As AL).
(8)
B' = BV (A A)V = AV + AV A'' = =[As + As AI),
(9)
C' = CV C[Vs =i]=[Vv, CV. = [Cs Ct).
(10)
Then, for the score matrices, we have:
C.
SA = AC = (AV) = A'C = [AAs 0)
= ASCS,
(11)
C
SB BC T B'C = AAs As A
(As + As)CS A
(12)
C
X
By the SVD construction, As has full column rank r and 7min (As) =o,(A) 0. Since As A Vs and Vs is orthogonal (i.e., Vslle 1), it follows that
A Aslla AV ll < A
(13)
According assumption (i), know ||l|| <
A or (A) 7min (As). The standard
minimum--inngula--alll value perturbation argument (or Weyl's inequality in spectral norm form) yields
that As + As s remains full column rank r. Since left multiplication by full-column-rank matrix does not change rank, it follows that:
rank( (X) rank
(ank((A))) ((As + As)C3) rank(C3)) rank(Css,
(14)
(S = rank rank(Ag C5 = rank (Cs) rank (X).
(15)
According to the assumption (ii,, there exists a set I of size r such that X, is a basis for col(X). Since X r, the dimension of the column space must be As result,
rank(S,)) rank(X) =
(16)
To prove rank(Sn) (Sn)(Sx), we construct linear operator (matrix) T that decouples the column space of X from the rest of the space. Let P == Px be the orthogonal projector onto :: coll X), Z (I-P)Y be the component of Y orthogonal X, and Pz, be the orthogonal projector onto col( Zz). We consider the linear operator:
T= (I -P,z)(( p)]
P
(17)
Since P projects onto col(X), PX= X, and (I-P)X- 0. Given Z = (I - -PY,, we have
PX + PY
= X+PY
TS₁ = T(x=) (I P2, - P)(+
(18)
(I (I-Pz,)
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Restricting to I U K, analyze the submatrix formed by columns indexed by I (size r) and K (size determined by k), which are disjoint:
XK + PYK (TSB) .UUKK = (I Pz, P Zk
+
X + PY,
Xx + PYk
=
(19)
(I- P2,
Since Pz, projects onto the column space 2, the residual (I Pz, Zz exactly zero. Because the matrix becomes block upper-triangular, its rank is the sum of the ranks of the diagonal blocks.
By Lemma the top-left block has rank r. By assumption (iv), the bottom-right block has rank k 2 1. Thus block-triangular rank additivity yields
rank ((TS)): r k.
(20)
Since the rank of a submatrix (columns 1 U K) is a lower bound for the rank of the full matrix, and left multiplication by T cannot increase the rank of the original matrix, we have the inequality:
rank(S 2 rank(TSp) 2 rank (ank((TSp).nnn (TSa).
(21)
Given k 2 and (S)) = r. we have:
(ank(SB) 2 r r= rrank(AA))
(22)
Here, we complete the proof.
Remark. We decompose A into two parts: one (Ag) that lies in the original row space of A, and another (A1) 1.) that introduces directions outside this space, Assumption (i) ensures the in-span perturbation Ag is not too large (controlled by (A)) so the original query directions in A are not destroyed by completion. Assumption (ii) asserts that we can select r columns from X that span U. This fixes stable r-dimensional basis for the existing subspace. Assumption (iii) claims that adding the projected perturbation PY, does not reduce the independence of these r columns. Thus the original r-dimensional structure is preserved. Assumption (iv) requires that there exist k 2 1 columns outside I whose orthogonal components (after removing projections onto both U and col Zz)) are linearly independent. These contribute genuinely new directions in Ut= Together, these assumptions ensure that the rank of SB contains at least the r preserved dimensions from U plus the h fresh orthogonal ones. Consequently, Sg can express more independent scoring patterns and has the ability to potentially make finer-grained distinctions.
A.2 RELATED WORK
A.2.1 LARGE LANGUAGE MODELS
Large language models (LLMs) are class of foundation models designed to process, understand, and generate natural language scale. With fine-tuning and prompting, these models excel across a variety of tasks, including text generation, summarization, reasoning, translation, and coding (De- vlin et al., 2019; Radford et al., 2019; Brown et al., 2020). Notable examples, such as LLaMA3 (Grattafiori et al., 2024), GPT-4o (OpenAl et al., 2024), and Qwen2.5 (Yang et al., 2025), contain billions parameters and are trained on extensive textual datasets. The large-scale pretraining en- ables them to capture complex contextual, semantic, and syntactic relationships in natural language. In this work, we utilize pretrained LLMs for query modification to tackle the proposed QCR task. By integrating quality information conditions, our model autonomously learns to generate quality- aware details for query extension. This provides users with multiple visible query suggestions, allowing them to explore diverse retrieval results.
A.2.2 VISION-LANGUAGE MODELS
Vision-language models (VLMs) have become the de facto foundation for image-text tasks, demon- strating exceptional potential across a variety of applications (Maniparambil et al., 2024; Dong et al., 2025; Huang et al., 2026). Pioneering work such as CLIP (Radford et al., 2021) and ALIGN (Jia et al., 2021) learn directly from raw texts about images by aligning them in a shared embedding space. CoCa (Yu al., 2022) combines contrastive loss with captioning loss to train image-text cncoder-decoder model, effectively integrating capabilities from both contrastive and generative ap- proaches. Blip2 (Li et al., 2023) bridges the modality gap with lightweight Q-Former improve
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pretraining efficiency. In this paper, we adopt joint-embedding VLMs like CLIP as foundation mod- els for text-to-image retrieval. Instead of fine-tuning the VLMs on target datasets, we keep them frozen and focus refining textual queries to achieve both quality improvement and control over the retrieved images. Improving existing VLMS for retrieval quality control is orthogonal to our approach and represents a promising direction for future research.
A.2.3 TEXT-TO-IMAGE RETRIEVAL
Text-to-image retrieval aims to identify the most relevant images from a database given natural language query (Chen et al., 2021; Lu et al., 2022; Pacini et al., 2024; Wu et al., 2025). It plays critical role in applications such as visual search, e-commerce, and content-based recommenda- tion. Recent advances in VLMs (Radford et al., 2021; Gao et al., 2022; Yu et al., 2022; Jia et al., 2021) have significantly improved performance on this task by learning powerful cross-modal rep- resentations. These models map images and texts into shared embedding space, typically through contrastive learning on web-scaled image-text pairs. However, existing retrieval systems are primar- ily optimized to return the top-k images that are semantically aligned with the input query. They overlook other crucial dimensions that strongly affect user satisfaction in practical scenarios, such as aesthetic appeal (Yi et al., 2023), interestingness (Gygli et al., 2013; Abdullahu & Grabner, 2024), or low-level visual quality (Wang et al., 2021; 2023a; Zeng et al., 2025). In this work, we advocate for incorporating quality control into retrieval. By allowing users to explicitly influence the quality attributes of the returned results, we enable a more personalized and controllable search experience, moving beyond simple semantic matching toward more adaptive, user-centric paradigm.
A.2.4 QUERY COMPLETION
Query completion (QC) aims to extend user short inputs, referred to query prefixes, by generating longer and more informative query completions. It is a widely used technique that helps users better articulate their intent and resolve potential query ambiguity. Traditional QC methods rely on factors such as user profiles, query libraries, and prior search history to extend prefixes into query completions, which limits their applicability unforeseen prefixes (Bar-Yossef & Kraus, 2011: Mitra & Craswell, 2015; Cai & De Rijke, 2016). Recently, several generative approaches have been proposed for query completion with arbitrary prefixes, primarily for text generation and document retrieval tasks (Lee et al., 2021; Wang et al., 2023b; Lei et al., 2024). QC-based methods for text-to-image retrieval remain scarce, with only few related works. Zhu al. (2024) enhance interactive image retrieval through query rewriting based on user relevance feedback, while Sun et al. (2024) leverage LLMS to generate product-aware query completions. Pacini et al. (2024) refine text queries using visual feedback to improve text-to-image retrieval performance. However, these approaches primarily focus on query suggestion rather than achieving control over the quality of retrieved images. In contrast, we tailor query completion to enhance retrieval quality, making the first attempt to adapt it to given search corpus for quality-controllable retrieval.
A.3 ANALYSIS OF DATASET DEPENDENCY
Our work focuses on text-to-image retrieval, where the goal is to retrieve relevant images from a fixed dataset based on textual query. This task is inherently dataset-dependent, as the retrieval process relics entirely on the available images within the dataset. Therefore, the query is crucial in this task: the more specific and detailed the query, the easier the retrieval system can match it to the corresponding image. Conversely, short or vague queries make it significantly more difficult for the system identify the intended image. That is why our proposed QCQC method aims to enrich the original short queries with more specific, quality-aware details. It it notable that these details are randomly generated. Instead, they are learned directly from the dataset itself by fine- tuning the LLM to fit the captions. As a result, the completed queries remain dataset-dependent and contextually relevant. The additional details are not unnecessary, as they provide essential guidance to the retrieval system, making it accurately identify images with desired quality.
A.4 ANALYSTS OF SCORE DIFFERENCES
In Tables 3 and 4, the average quality scores across low, medium, and high conditions may appear close for set of short queries. This is expected due to dataset limitations. As shown in Figure
16

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Published as a conference paper at ICLR 2026
Table Retrieval quality with five quality levels on CoCa.
Relevance (Red + Red)
M
VL
M
H
VH
4.581
4.551
4.559
4.579
4.507
Aesthetics (Green + Green)
VL
0,355
0.364
0.372
0.376
0.382
4.870
4.792
4.784
4.748
4.718
0.357
0.363
0.370
0.376
0.383
4.882
4.954
4.863
4.849
4.820
0.356
0.366
0.371
0.377
0.381
5.054
5,048
5,005
5,019
4.998
0.355
0.362
0.371
0.370
0.381
5.159
5.166
5.161
5.135
5,084
VH
0.352
0.366
0.369
0.373
0.386
the relevance scores across both Flickr2.4M and MS-COCO are not uniformly distributed, and images with extremely low or high scores are rare. For instance, on Flickr2.4M, the relevance scores range from 0.252 to 0.562, and the entire span across the whole dataset only about 0.3 (where the extreme values correspond to two images from different classes). When retrieving images for single query, the available results often fall within a narrower score range (much smaller than 0.3) because the dataset lacks images at both ends of the quality spectrum (sparsely distributed). For example, if all images retrieved from the query "a dog" have aesthetic scores between 3.8 and 4.7 (due dataset limitations), even under the "High" condition, the best available image might score 4.7 which lies in low range (given the whole range [2.782, 6.961]). But is still higher than the score of 3.8 under the "Low" condition. Thus, the method is still effectively ranking and retrieving better images within the constraints the dataset.
Despite this dataset-level constraint that limits the score differences. method demonstrates effec- tive ranking ability and a consistent, meaningful trend. As shown from left to right in Tables 1 and 2, both the retrieved image scores and their visual appeal improve progressively dition increases. This pattern is further supported by quantitative results in Tables 3 and 4, where the average quality scores clearly increase across the low, medium, and high conditions. This behavior cannot be reproduced by baseline methods that lack quality consideration in retrieval.
the quality
A.5 ADDITIONAL EXPERIMENTAL RESULTS
In Table 7, we present the results on the ickk2. 4M dataset across five quality levels using CoCa the caption model. Tables and 9 presents the quantitative results of our method using GPT2 (Radford et al., 2019) as the backbone. In addition, we provide more qualitative comparison on the two datasets in Tables 10-12. These results further demonstrate that the proposed QCQC method enables controllable retrieval quality. In rare cases, the completed queries may not align with the semantics of the query prefixes. This occurs when the query completion model generates sentence referencing different objects. Refer to Table 13 for examples of such cases.
A.6 LIMITATION
The relevance and aesthetic quality of the retrieved images depend on the reliability of the VLMs and aesthetic evaluation models. If these models are not sufficiently reliable, retrieval performance can be significantly affected. addition, the model needs to perceive image quality within the datasets to achieve quality control in retrieval. However, the retrieval datasets may sometimes lack the gran- ularity needed to differentiate between high-quality and low-quality images. In some instances, the retrieval database may not contain high-quality or low-quality images that match specific queries.
A.7 THE USE OF LARGE LANGUAGE MODELS (LLMS)
In preparing this paper, large language models were used as writing assistants for grammar checking and minor sentence rephrasing. All technical aspects of the work, including the design, implemen-
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Published as a conference paper at ICLR 2026
Table Retrieval quality of various methods on Flickr2. 4M. CoCa and Blip2 are used gener- ate textual descriptions; (Low), M (Medium), and H (High) indicate the quality conditions; and Ctrl specifies whether the method enables controllable retrieval over quality. For both average relevance (Ave Rel) and average aesthetics (Ave Aes), higher values indicate better retrieval quality.
Aes Cond Rel Cond
Quality
VLM
Ctrl ?
M
H
Ave Aes Ave Rel
4.735
4.735
4.735
4.735
4.735
4.735
4.735
4.735
4.735
Prefix
0,350
0.350
0.350
0.350
0.350
0.350
0.350
0.350
0.350
x
Ave Acs
4.730
4.822
4.831
4.823
4.837
4.784
4.798
4.722
4.842
LLaMA3
Ave Rel
0,351
0,351
0.351
0.353
0.351
0.350
0.354
0.354
0,352
x
Ave Aes
4.359
4.651
4.728
4.712
4.668
4.791
4.791
4.816
5.056
GPT-4o
Ave Rel
0.378
0.361
0.357
0.358
0.360
0.356
0.361
0.357
0,361
x
Ave Aes
4.681
4.639
4.673
4.688
4.504
4.654
4.610
4.556
4.692
PT
Ave Rel
0.351
0.344
0.350
0.350
0.346
0.347
0.349
0.352
0.352
x
Aes
4.848
4.818
4.864
4.847
4.827
4.876
4.829
4.896
4.853
FT
CoCa
Ave Rel
0.366
0.365
0.367
0.365
0.363
0.366
0.367
0.366
0.368
x
Ave Acs
4.646
4.674
4.632
4.878
4.921
4.894
5.182
5.095
5.124
Ours
CoCa
Ave Rel
0,354
0.372
0.382
0.355
0.369
0,386
0.357
0,366
0.385
Ave Acs
4.838
4.674
4.744
4.592
4.599
4.772
4.727
4.749
4.818
FT
Blip2
Ave Rel
0,369
0.360
0.369
0.365
0.362
0,365
0,373
0.359
0,368
x
Ours
Blip2
Ave Aes
4.528
4.560
4.470
4.948
4.946
4.885
5.266
5.160
5.236
/
Ave Rel
0.355
0.374
0.393
0.354
0.374
0.391
0.354
0.367
0.387
Table 9: Retrieval quality of various methods on MS-COCO, where L (Low), M (Medium), and (High) indicate the quality conditions for retrieval, and Ctrl specifies whether the method enables controllable retrieval over image quality. For both average relevance (Ave Rel) and average aesthet- ics (Ave Aes), higher values indicate better retrieval quality.
Quality
Aes Cond
L
M
M
H
Ctrl
Rel Cond
M
M
H
Ave Aes
4.817
4.817
4.817
4,817
4.817
4.817
4.817
4.817
4.817
Prefix
Ave
0.349
0.349
0.349
0.349
0.349
0.349
0.349
0.349
0.349
x
Ave Aes Ave
4.903
4.891
4.855
4,916
4.875
4.880
4.871
4.858
4.911
LLaMA3
0.348
0.349
0.347
0.348
0.349
0.347
0.348
0.350
0.344
x
Ave Aes
4.673
4.754
4.686
4.782
4.808
4.880
4.838
5,075
5.048
GPT-4o
Ave Rel
0.371
0.357
0.354
0.360
0.358
0.350
0.359
0.352
0.351
Ave Aes
4.742
4.731
4.855
4,821
4.775
4.854
4.830
4.726
4.847
PT
Ave Rel
0.347
0.345
0.350
0.349
0.344
0.345
0.351
0.347
0.344
x
Ave Aes
4.785
4,820
4.866
4,813
4.852
4.888
4.833
4.919
4,960
FT
Ave Rel
0.369
0.369
0.373
0.373
0.369
0.373
0.367
0.376
0.372
x
Ave Aes
4,890
4,889
4.793
4,885
4.939
4,903
4,950
5,004
4.898
FT-CoCa
Ave Rel
0.347
0.348
0.356
0.346
0.349
0.352
0.347
0.349
0.351
Ave Aes
4.776
4.883
4.824
4.914
4.968
4.873
$.039
4.967
5.053
FT-Blip2
Ave Rel
0.349
0.351
0.352
0.344
0.349
0.350
0.343
0.349
0.349
x
Ours
Ave Acs
4,896
4.809
4.719
4,973
4.879
4,916
5.017
5,020
5.109
V
Ave Rel
0.354
0.365
0.385
0.356
0.368
0.387
0.353
0.368
0.391
tation, and verification of experiments and analyses, were carried by the authors. In our experi- ments, we include the LLaMA-3 (Grattafiori et al., 2024) and GPT-4o as query completion models (OpenAl et al., 2024) for empirical comparison.
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Published as a conference paper at ICLR 2026
Table 10: Query completions with their retrieved images and quality scores on MS COCO
Rel: Low, Aes: Low
Rel: Medium, Aes: Medium
Rel: High, Aes: High
wine glass next to plate with some fish and veggies it
wine glass next plate with some meat and vegeta- bles on it Aes 5.209, Rel 0.376
wine glass and three clocks all set at different times Aes 5.555, Rel 0.417
Aes 4.790, Rel 0.350
toothbrush on a table with bunch of scissors Aes 4. 1,14 Rel 0.345
a toothbrush that is on down the counter Aes 4.837, Rel 0.370
a toothbrush with a smiley fuce sitfing on sink
Aes 5.184, Rel 0.412
a backpack with rollers is sit- ting unattended in the middle of this forested dirt road Aes 5.231, Rel 0.470
backpack and a line of supplies laying out
a backpack some water rocks and plants
Aes 3.937, Rel 0.355
Aes 5.130, Rel 0.374
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Published as a conference paper at ICLR 2026
Table 11: Query completions with their retrieved images and quality scores on Flickr2. 4M
Rel: Low, Aes: Low
Rel: Medium, Aes: Medium
Rel: High, Aes: High
train on a track next to grassy field
train station with people waiting to board bus Aes 4.910, Rel 0.380
train in the desert
Aes 4.585, Rel 0.369
Aes 5.488, Rel 0.391
horse drawn carriage on dirt road
horse drawn carriage with people on it Aes 5.023, Rel 0.3773
horse is grazing in field under a cloudy sky Aes 5.207, Rel 0.390
Aes 4.718, Rel 0.357
a truck is parked in front of building
a truck is parked under a bridge Aes 5.070, Rel 0.370
a truck is parked in fromt of the washington monument Aes 5.335, Rel 0.389
Aes 4.800, Rel 0.331
boat docked in the water next to other boats Aes 3.814, Rel 0.358
boat in the water near castle Aes 4.839, Rel 0.371
boat on the water with buildings in the background Aes 5. 5.113, Rel 0.396
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Published as a conference paper at ICLR 2026
Table 12: Query completions with their retrieved images and quality scores on MS COCO
Rel: Low, Aes: Low
Rel: Medium, Aes: Medium
Rel: High, Aes: High
an aeroplane flying in the air with big blue sky be- hind it
Query: an aeroplane flying over the beach and two guys standing on it Aes 5.516, Rel 0.425
an aeroplane flying high on clear sky Aes 4.739, Rel 0.360
Aes 4.536, Rel 0.354
ADOUIA
fire hydrant stands in front of bald eagle wall mural Aes 4.991, Rel 0.355
a fire hydrant sitting in front of sign for cafe Aes 5.511, Rel 0.368
a fire hydrant is painted to look like dalmation Aes 5.799, Rel 0.447
Pem!o Grcce
a toilet with a raised lid in some lavatory Aes 4.457, Rel 0.361
a toilet and sink in a small bathroom with seat up Aes 4.502, Rel 0.372
a toilet is sitting outside with sign it Aes 5.264, Rel 0.394
sheep is standing on sheep and baby sheep white fence
sheep dog herding sheep through a grass field Aes 5.213, Rel 0.388
standing in a field
Aes 4.557, Rel 0.358
Aes 5.014, Rel 0.378
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Published as a conference paper at ICLR 2026
Table 13: Some bad retrieval cases the two datasets
Rel: Low, Aes: Low
Rel: Medium, Aes: Medium
Rel: High, Aes: High
toilet sits next to shower an sink
toilet with wooden seat on top of it Aes 5.226, Rel 0.371
toilet in between two trash cans Aes 5.551, Rel 0.434
Aes 4.192, Rel 0.361
an apple phone and some other type of machine Aes 3.586, Rel 0.361
an apple and other fruit are sitting together Aes 5.007, Rel 0.373
an apple with knife stuck into it dripping blood
Aes 5.484, Rel 0.395
orange and blue bath- room with a tub sink and toi- let
orange cat with its eyes closed sitting next to books
an orange and black fire hy- drant sitting close to a curb
Aes 4.1 187, Rel 0.357
Aes 4.434, Rel 0.359
Aes 5.603, Rel 0.393
HISCING LEDON
a clock tower with a statue in front of Aes 5. 187, Rel 0.376
a clock tower with a cross on
clock on the wall of room Aes 4.578, Rel 0.355
top Aes 5.425, Rel 0.388
22


Reviews:
[{"review_id": "9M3z9Etnbn", "reviewer": "Reviewer_7fCB", "summary": "This paper explores using an LLM to rewrite short text queries into more detailed, quality-aware descriptions, aiming to control the aesthetic and relevance level of retrieved images without modifying the vision-language model. The method learns to expand queries based on quality labels and shows that higher-quality prompts lead to higher-quality retrieval results.", "questions": "1. In real retrieval scenarios, queries often contain multiple objects, relations, and context. How would the method scale to complex, natural multi-entity queries?\n2. How would the method perform when the image databse diversity is limited, or when aesthetic cues are less meaningful (news/fashion domain)?\n3. The theory suggests increased rank leads to better discrimination. But multiple  multiple LLM rewrites could also increase rank. On theoretical, what is the concrete advantage of your controlled rewriting over simple rewriting ?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The motivation is clear. The paper introduces aesthetic cues to explicitly control and improve retrieval quality. \n2. The method is plug-and-play and easy to apply in existing systems, requiring no modification to the visual model.", "weaknesses": "1. Experiments mainly use single-word, single-object queries（e.g., “a dog”）. Real retrieval queries are usually longer, involve multiple objects and relations. Current setup looks more like keyword/entity retrieval.\n2. The paper doesn't report preprocessing cost or inference latency, so it's hard to judge the efficiency of method.\n3. The approach assumes the database has many visually similar images with different aesthetic qualities (like Flickr30k/COCO). In many datasets this won’t hold (VisualNews,fashion200k).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "jWBk3CLa96", "reviewer": "Reviewer_e8i9", "summary": "This paper proposes a new method for text-to-image retrieval called quality-conditioned query completion . The method addresses the problem of short and underspecified user queries. It uses a large language model to expand short queries with descriptive and quality-aware details. The approach does not modify pretrained vision–language models and provides interpretable, controllable retrieval behavior. Experiments on MS-COCO and Flickr2.4M show that QC² consistently improves both semantic relevance and visual quality, outperforming existing baselines.", "questions": "1. How does the method perform on the datasets from the LoTLIP[1]?\n2. How does the scalability of the QCR method which requires an LLM to be fine-tuned for each gallery, comparing to VISA [2], a \"plug-and-play\" method that performs re-ranking at test-time without training?\n\n[1] LoTLIP: Improving Language-Image Pre-training for Long Text Understanding\n\n[2] Visual Abstraction: A Plug-and-Play Approach for Text-Visual Retrieval", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper proposes a new task: quality-controllable text-to-image retrieval, which considers both semantic relevance and aesthetic quality during retrieval. This setting aligns well with real-world search scenarios.\n2. The method uses a large language model to generate query expansions with quality cues, requiring no modification to existing vision–language models and remaining relatively simple to implement.", "weaknesses": "1. The paper mainly focuses on short queries but does not evaluate on datasets with long or descriptive queries. When the textual input already contains sufficient information, the effect of query completion may diminish or even introduce redundancy or semantic drift. Will this strategy still work for underspecified long user query?\n2. The paper's quantitative evaluation relies on a limited set of test queries, namely 80 concrete object nouns. Consequently, the method's performance in handling more abstract, complex, or queries involving emotional atmospheres (such as \"a sense of calm\"，\"a vintage vibe\") remains entirely unvalidated.\n3. This method requires fine-tuning a large language model for each specific image retrieval gallery. This means it is not a \"plug-and-play\" solution. On the contrary, if the gallery is replaced or updated, the dataset must be rebuilt and significant resources must be invested in retraining, which severely limits its scalability.\n4. How can this be extended to image-to-text retrieval?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "tZ37KrVteb", "reviewer": "Reviewer_syQK", "summary": "This paper proposes Quality-Controllable Retrieval (QCR), which allows users to control retrieval results through quality dimensions such as relevance and aesthetics. The proposed approach uses an LLM as a query completion function conditioned on discretized quality levels. Experiments on multiple datasets demonstrate that quality-aware query completions can enhance retrieval performance, although only two quality metrics are explored.", "questions": "- The training data consists of concise sentence summarizing the main content of the image with annotated aesthetic and relevance levels. Through training, the LLM learns the relation between text descriptions and the two quality metrics. However, I am concerned **whether such concise sentences are sufficient for the LLM to truly capture the connection between these quality metrics and the actual visual content of the image.**\n\n- Although the numerical results suggest separability, they do not always match intuitive perception. For example, in Table 1 (teddy bear), the left image (aesthetic 4.788, relevance 0.359) seems more aesthetically pleasing and more representative of a teddy bear than the right image (aesthetic 5.818, relevance 0.437). This raises concerns about the reliability of the scoring process.\n\n- The authors claim that the method can be extended to many different metrics. However, the paper only provides experiments with relevance and aesthetics.  **I wonder how the LLM behaves when more metrics (three or more) are involved. Would the model prioritize certain metrics while ignoring others? Will the LLM's completion ability degrade under multiple metrics**？\n- Using LLM for query completion is a clever choice, but while the authors claim to avoid hallucinations, no explicit mechanism is provided. Have the authors  observed redundant, voague, or inaccurate queries, and were any filtering strategies considered?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- The paper has a good motivation, addressing the challenge of underspecified queries that often lead to ambiguous retrieval results. Quality-controllable retrieval is both practically useful and of research interest.\n- The paper is well-structured, and the methodology is described in sufficient detail.", "weaknesses": "- While the concept of quality-controllable retrieval is attractive, the current work only explores two quality dimensions (relevance and aesthetics). This limited scope is insufficient to demonstrate the true practical value of controllable retrieval in applications.\n- Despite the claim that LLM-based completions avoid irrelevant or hallucinated content, there is no explicit mechanism or evaluation to manage or detect query artifacts that could mislead retrieval or introduce out-of-distribution details.\n- The use of large language models for query rewriting or expansion is already a common and mainstream approach. The main innovation of this paper lies in introducing two quality indicators and training data to guide LLM-based query completions. My primary concern is that this level of novelty does not meet the expected standards of ICLR.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "WbBWbZtuG2", "reviewer": "Reviewer_PhGu", "summary": "This paper introduces the problem of Quality-Controllable Retrieval, which is an extension of the text to image retrieval problem where in the input query can have additional constraints such as high relevancy, low aesthetics, etc and the goal is to retrieve images from a gallery which satisfy the original query and the additional constraints as well. To enable existing VLMs such as CLIP to do this, they use a query augmentation pipeline where they train an LLM to generate modified queries based on certain additional constraints (relevancy, aesthetics in their case). The trained LLM is then used during inference time to change the query based on the additional constraints. Experiments on MS-COCO and a new Flickr2.4M dataset show the method can effectively steer retrieval results. Conditioning on \"High\" quality levels yields images with significantly better average aesthetic and relevance scores compared to \"Low\" conditions or baseline methods.", "questions": "refer to weaknesses section for questions", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The paper tackles a practical and significant problem. Users frequently use short, ambiguous queries and most existing T2IR systems just return the top-k images by cosine similarity rather than controlling for additional quality metrics. The formulation of \"quality-controllable retrieval\" is a strong and useful contribution. \n2. Simple and elegant solution: their approach allows to convert any existing VLM into a quality controllable one by using a fine-tuned LLM which can augment the input text query to constrain the search. \n3. Good visualizations / interpretable results: The paper contains several qualitative examples which show that by fine-tuning the LLM to augment queries, they are able to get precise augmented queries where the retrievals which match the users preferences.", "weaknesses": "1. The same CLIP model and EV_A model are used to generate the training data quality labels and for measuring the average metrics during evaluation as well, this might lead to the fine-tuned LLM learning patterns which work only for a particular gallery/dataset and might overfit the query augmentations to that dataset. It would be great if the authors can either do a small human study or use other stronger SoTA multi-modal models as relative judges of the metrics. \n2. Missing per-condition re-rank baselines in table 6: the paper only compares with a retrieve -> sort by aesthetics post filtering pipeline in Table 6. This effectively is only covering the H/H sub-segment. Since the main idea behind the paper is QC^2, the table should also include the (L/M/H X L/M/H) segments like in Table 3/4 and also include stronger retrieval baselines like the following:\na) a weighted joint scorer which ranks the images based on a weighted average between rel and aes distance with the queries gt_rel and gt_aes\nb) constrained retrieval: since the rel and aes values are already binned, they can first be used to filter out all images which do not belong in the gt bin and then performing search over the reduced sub-space. \nThese results should help shed more light into how simple retrieval methods perform against more complex query augmentation methods \n3. Dataset dependency: As already mentioned by the authors, for each new dataset, they need to fine-tune the query augmentation LLM again so that the LLM can learn the specifics of rel and aes scores and the corresponding captions. This questions the scalability of the method if it needs to be deployed in a real scenario where web-scale retrieval is performed which has a lot of noise/distribution shift. \n\n4.Mode-collapse/diversity risk: QC^2 relies on an LLM to generate quality-conditioned completions, yet the paper reports no diversity or entropy analysis of those texts. Without such checks, the LLM may collapse to a few stylistic templates which led to low loss with a fixed rel and aes value, which can lead to poor diversity in the images which the users would see. It would be great if the authors can provide some metrics on the diversity of queries generated per (rel,aes) tuple. One such metric can be follows: \nfor a fixed instruction (query: q, rel: r, aes: a), sample n different completions from the llm with different temperature parameters, and then check how diverse these completions are (dispersion based on text-clip distance can be a good metric).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)