Automatic Synthetic Data and Fine-grained Adaptive Feature Alignment for Composed Person Retrieval
Authors: Delong Liu
Haiwen Li
Zhaohui Hou
Zhicheng Zhao
Fei Su
Yuan Dong
Venue: NeurIPS 2025 poster ()
Keywords: Composed Person Retrieval
Synthetic Data Generation
Fine-grained Feature Alignment
Multimodal Retrieval
Primary area: applications
TL;DR: We introduce a large-scale synthetic dataset and a fine-grained alignment framework for composed person retrieval, and provide a manually annotated benchmark test set for objective evaluation.

Abstract: Person retrieval has attracted rising attention. Existing methods are mainly divided into two retrieval modes, namely image-only and text-only. However, they are unable to make full use of the available information and are difficult to meet diverse application requirements. To address the above limitations, we propose a new Composed Person Retrieval (CPR) task, which combines visual and textual queries to identify individuals of interest from large-scale person image databases. Nevertheless, the foremost difficulty of the CPR task is the lack of available annotated datasets. Therefore, we first introduce a scalable automatic data synthesis pipeline, which decomposes complex multimodal data generation into the creation of textual quadruples followed by identity-consistent image synthesis using fine-tuned generative models. Meanwhile, a multimodal filtering method is designed to ensure the resulting SynCPR dataset retains 1.15 million high-quality and fully synthetic triplets. Additionally, to improve the representation of composed person queries, we propose a novel Fine-grained Adaptive Feature Alignment (FAFA) framework through fine-grained dynamic alignment and masked feature reasoning. Moreover, for objective evaluation, we manually annotate the Image-Text Composed Person Retrieval (ITCPR) test set. The extensive experiments demonstrate the effectiveness of the SynCPR dataset and the superiority of the proposed FAFA framework when compared with the state-of-the-art methods. All code and data will be provided at https://github.com/Delong-liu-bupt/Composed_Person_Retrieval.

Automatic Synthetic Data and Fine-grained Adaptive Feature Alignment for Composed Person Retrieval
Delong Liu', Haiwen Li', Zhaohui Hou², Zhicheng Zhao
(344 Fei Su Yuan Dong'
Beijjng University of Posts and Telccommunications "SenseTiime
Beijing Key Laboratory of Network System and Network Culture *Key Laboratory of Interactive Technology and Experience System, Ministry of Culture and Tourism (liudelong, lihaiwen, zhaozc, sufei, yuandong) (wpggg.bbpp.com edu. cn
com
a) Various Person Retrieval Tasks
Image-based based Person Retrieval Clothes- Channinn
Text-based Retrieval
Composed Retrieval (New)
Query
Target
Query
Target
Query
Torget
Query
Target
mon wearing vest, black
Wearing a light purple shirt.
blue shirt
Prompt Generator
Quality Filter
Image: Relative Caption Imagez
Image;
Relative Caption Image:
Instructions
Wearing work wash loffers, sitting bench in park
Wearing n grown lany- Sancais, and sitting on grasty
LLM
Scores
Weering black onfice boots. standing in front of
Weaning F-shirt, white sneakers playing
Instructions
MLLM
Weoring solid Reckisce, and corrying a green beg
Wearing gray
Prompts
Triplets
High-Quality Triplets
FL LORA
A
Weoning checksned
sweeter, wearing white sitting on e woodien
Training
bleuse, Red harddbyg
SynCPR
EAFA
Encoder 28
Q-former
Wecring black unked botl's haldiny lange 203
Wearing tweed bag.
Infarsnce
ITGPR
surt, blck baller carrying small block handong
Ne cap, holding a block leather wallet.
b) High-quality Triplets Generation
c) Some Examples from the SynCPR Dataset
Figure I:
Overview our contributions. (a) Comparison the proposed composed person retrieval task with several classic person retrieval tasks. (b) Illustration of the proposed automatic high-quality CPR data synthesis pipeline, the proposed training framework FAFA, and the first carefully annotated test set in this domain, ITCPR. (c) Some examples from fully synthetic SynCPR dataset.
Abstract
Person retrieval has attracted rising attention. Existing methods are mainly divided into two retrieval modes, namely image-only and text-only. However, they are unable make full use of the available information and are difficult to meet diverse application requirements. To address the above limitations, we propose new Composed Person Retrieval (CPR) task, which combines visual and textual queries to identify individuals of interest from large-scale person image databases. Never- theless, the foremost difficulty of the CPR task is the lack of available annotated datasets. Therefore, we first introduce a scalable automatic data synthesis pipeline,
"Corresponding author: Zhicheng Zhao.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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which decomposes complex multimodal data generation into the creation of textual quadruples followed by edentity-consister image synthesis using fine-tuned gen- erative models. Meanwhile, a multimodal filtering method ís designed to ensure the resulting SynCPR dataset retains 1.15 million high-quality and fully synthetic triplets. Additionally, to improve the representation of composed person queries, we propose a novel Fine-grained Adaptive Feature Alignment (FAFA) framework through fine-grained dynamic alignment and masked feature reasoning. Moreover, for objective evaluation, we manually annotate the Image-Texx Text Composed Person Retrieval (ITCPR) test set. The extensive experiments demonstrate the effectiveness of the SynCPR dataset and the superiority of the proposed FAFA framework when compared with the state-of-iee th- methods. All code and data will be provided at battps://gither com Soom/Deeoogee liu wwpt Cotp:sedmddddd..cc. Sermpposed_eroooo Person Retrieval
Introduction
Person retrieval 2 aims to identify target individuals from large-scale databases and encompasses two primary research directions: image-based person retrieval (IPR) 3 and text-based person retrieval (TPR) Typically, they rely independently on images or textual queries to identify the intended targets. In fact, in real-world scenarios, visual and textual information are often simultaneously available when searching for specific individuals. For example, when looking for a missing person, people may refer to the past photographs along with recent verbal description. However, existing methods fail fully exploit this combined information, resulting in suboptimal retrieval accuracy.
To address this drawback, as shown in Figure aa), a novel task named Composed Person Retrieval (CPR) is introduced, which fuses visual and textual information for person retrieval. Similar to Composed Image Retrieval (CIR) G) the CPR data will also comprise numerous triplets (7g. Tg. I.), where each triplet consists of reference person image (l2), a relative caption (T₂). and one or more target images4 (l,). The objective is to effectively locate I, by exploiting the complementary information between I and T.. Constructing such data requires paired images of individuals with the same identity (ID) and textual descriptions highlighting their differences. However, manual collection and annotation is mone-consuming, costly, and often hindered by privacy issues, limiting both the variety and scale of the depicted scenarios. Consequently, this poses significant challenge to the construction of comprehensive, high-quality, large-scale training dataset for CPR task.
To cope with these challenges, we propose a scalable automatic CPR data synthesis pipeline, depicted in Figure bb). The generation of complex multimodal triplets is achieved by overcoming two key problems: First, how to create pure and diverse textual data. Second, how to leverage the generative models to transform subset of this text into identity-consistent person images, thus attaining CPR data synthesis. Specifically, this pipeline is decomposed into three stages. First, a Large Language Model (LLM) (7) generates abundant textual quadruples, and each one comprises two image descriptions and two relative captions that connect them. Through carefully designed prompts, the LLM guided to produce diverse descriptions reflecting a wide range of individuals and states, while effectively capturing relative differences.
In order solve the second problem, we first fulfill the synthesis of person image- pairs in the second stage. Considering that directly employing pretrained diffusion models to individually generate I,, and I, will lead to identity mismatches and discrepancies from real-world distributions. Thus, we fine-tune generative models [8] [9] using real-world data 4 to derive a suitable person image generator firstly. Subsequently, by merging textual prompts, simultaneously generate single image containing two related sub-images, which are then cropped into the reference image and the target one, thereby ensuring identity consistency.
In the third stage, rigorous data filtering method is designed to ensure the high quality of triplets. Specifically, multimodal large language model (MLLM) 100 is applied to evaluates the generated triplets according to four scoring criteria: image quality, identity consistency, text-image alignment, and relative caption quality. After filtering based on these scores, high-quality, fully synthetic CPR dataset named Synthetic Composed Person Retrieval (SynCPR) can be obtained, and its representative examples are shown in Fi)
Moreover, we propose a novel framework tailored for CPR task: Fine-grained Adaptive Feature Alignment (FAFA). FAFA strengthens model training by integrating fine-grained dynamic alignment

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with bidirectional masked feature reasoning. thereby generating more comprehensive, robust, and fine- grained representations. Finally, in order conduct an objective evaluation of FAFA's performance, an Image-Text Composed Person Retrieval (ITCPR) test set is carefully constructed and manually annotated, based on widely-used clothes-changing person retrieval datasets such as Celeb-relI) []]. LAST 12. and PRCC [3]. Among them, we annotate the relative captions by selecting images of the same identity in different outfits or states, and ultimately form complete triplets. Extensive experiments ITCPR dataset demonstrate the effectiveness of both the proposed automated triplet synthesis pipeline and the FAFA framework. The main contributions can be summarized as follows:
A novel crosl- task, composed person retrieval is proposed for the first time, aiming to address person retrieval by making full use of combined visual-textual information.
A scalable automatic triplet synthesis pipeline is presented, which greatly alleviates the diffi- culties in CPR data annotation. Based on this pipeline, the first million-scad scale, high-quality and fully synthetic CPR dataset named SynCPR is constructed.
A new CPR framework, called FAFA is proposed, which significantly improves retrieval per- formance through fine -rained dynamic alignment and bidirectional masked feature reasoning.
The first carefully annotated test set named ITCPR is constructed, and extensive experiments validate the effectiveness of our proposed methods.
2
Related Work
Person Retrieval. Person retrieval primarily comprises two research directions: IPR and TPR. IPR
has been extensively explored from various perspectives, including feature extraction 3 14 15).
metric learning [16]. lightweight architecture design [17119], multi-branch frameworks 1 20 and
attention mechanisms 21 22). A related subtask, clothes-changing image person retrieval (CC-IPR)
[[]. targets identification across outfit variations and has driven the development of specialized datasets [3 12) and methods (53 In comparison, TPR emerges later but has progressed rapidly. It focuses on aligning visual and textual features within unified embedding space. Early TPR approaches emphasize global (26 and local [39-33] feature extraction and employ cross- modal matching losses [44] but often have difficulty in balancing efficiency and accuracy. More recently, visual- pretrained (VLP) models [35] 38 have significantly improved retrieval performance through carefully designed auxiliary tasks (39 40 [ tailored specifically for TPR fine-tuning. However, despite substantial progress, existing approaches still struggle to effectively integrate visual and textual information for precise identification of specific individuals, which remains an essential and practical requirement. To bridge this gap, we propose the CPR task.
Composed Image Retrieval. CIR 41 as representative compositional learning task 44. 45) jointly leverages image and textual queries for precise image retrieval. CIR has been extensively applied in fashion [S] and real-world domains 6 46), fostering diverse image-text fusion and training heavily depend on annotated triplet datasets, inherently limiting their generalizability. To alleviate reliance on annotation, recent zero-shot CIR (ZSCIR) approaches [49 propose techniques such as image-to- to---oooo--oeeee conversion
strategies. However, existing supervised CIR methods 47 48
4
49
46
50
51 or using LLM--genrrter target descriptions to reformulate CIR as pure text-to-image
retrieval 52 53 Compared to CIR, CPR imposes stricter constraints on image relevance and places
greater emphasis on fine-grained variations during retrieval. Consequently, existing CIR methods generally struggle to maintain effectiveness under the CPR setting.
Diffusion Models. Diffusson models [54] (55 have become the prevailing architecture for image generation, with applications in text-to-image synthesis (56/58), image translation [59)61), and
controllable content generation 62 64 This progress has been accompanied by efficient parameter tuning strategies such as Low-Rank Adaptation (LoRA) [9] and Adapter based [65] methods, which retain high generation quality while enhancing adaptability. The incorporation of Transformer [66]
architectures has led to novel designs like the Diffusion Transformer (DiT) 67 improving scalability
and bring about advanced models such as Stable Diffusion 3 [68], PixArt (69, and Flux [8. Inspired
by the above, our work elegantly combines the Flux model with LoRA-basee fine-tuning to generate person images that closely resemble visual styles in the real world.
3

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Diffusion Layers
LoRA layers
Frozen
Learnable
Cosine Similarity
Stage 1: Diversified
Stage 2: Person Image Pair Synthesis
Stage 3: Data Filtering
Quadruple Generation
Relative Captions
Weaning nhile cardgen.
Raw Triplet Pair
SynCPR Dataset
Wearin by dase juste.. ewokert
Instruction
Crop
TPR
Prompts
Dataset
(Color
Inference
Training
Filtering
Fidelity
Template
T
Consistency
(Character)
(0,1)
Diffusion
Alignment
Quality
Qwen2.5.VL
Examples
a) High-quality CPR Data Synthesis Pipeline
Diverse
Similarities
Match Labels
Predict
fe
2
4
E
0
0.5
Align
Pooling
[CLS]
Proj.
Meen
merg
Sum b4a
Image Encoder
Image Encoder
Q-former
Fine-grained Dynamic Alignment
bmfr
Wearing white cordigan, loafers.
D
Latent
Learnable Queries
Mask
Decoder
D
Relative Caption (For Query Side)
Bidirectional Masked Feature Reasoning
Reference Image
Target Image
b) Fine-grained Adaptive Feature Alignment Framework
Figure 2: Overall framework of our method. (a) The pipeline for synthesizing high-quality triplets, consisting of three key stages: generation of text quadruples, synthesis of person image pairs, and data filtering. (b) The structure of FAFA. The left part illustrates the training process of the model, while the right part highlights the key objectives employed by FAFA.
3 Method
The overall framework of the proposed CPR method is illustrated in Figure comprising two main components. Section 3.1 introduces the automatic pipeline for synthesizing high-quality CPR data, including textual quadruple generation, identity-consisee 3.2 presents the FAFA framework, detailing the model architecture, fine-grained dynamic alignment objectives, bidirectional masked feature reasoning strategies during training, and the inference procedure. To objectively evaluate the proposed method, Section 3.3 outlines the construction of the ITCPR test set.
image synthesis, and data filtering. Section
3.1 High-quality CPR Data Synthesis
Diverse Textual Quadruples Generation. Considering that there is currently a lack of feasible methods for directly generating multimodal triplet data, we propose decomposing this task into the generation of angle-modality triplets first and then expanding them into multimodal form, which effectively alleviates this problem. Specifically, an instruction template P(Character, Clothes, Color) designed to guide the LLM 7 (denoted as Gumm (-)) ))) to produce textual quadruples. Each quadruple comprises two pairs of textual triplets, as expressed in Equation] [
Gum(p) > (Ti,- Ty Te-sy, Ti,),
(1)
where Ti, and Ti, denote the same person with different outfits or states and will later be used to synthesize images Ig and 1, The relative caption T highlights key appearance changes from Iy to It while the reverse caption Tr-zy describes changes the opposite direction, allowing two usable triplets to be constructed from each quadruple. To enhance diversity and avoid repetitive outputs, each instruction p ~ P includes multiple descriptive elements and randomly selected high-quality annotated examples. Providing these random elements and examples ensures semantic richness, diversity, output quality, and structural stability (see Appendix A.T details). simplified version of the instruction template is shown below:

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Inconsistent x
Generation method
Pre-trained Flux.1
(Unrealistic x
Finetuned Flux.1 ( Realistic
Prompt: Ti,
FLux. 1
Prompt: Ty,
FLux.1 1
Consistent
Prompt: Rechongular grid
layout for... Left: 7. Right: T,
FLux.1
Figure 3: Example pairs of generated person images using different generative models and generation methods under the same text input.
Generate a quadruple satisfying CPR requirements using the following elements: suggested character, clothes, color. Follow the output format and content length from: examples.
Identity-consistent High-quality Image Synthesis. As mentioned before, generative models have been widely applied to text-to-image synthesis. However, most of them are oriented towards natural images and portraits, and methods specifically for generating pedestrian images are still rare. Therefore, person images generated by pretrained models often deviate significantly from the style and distribution of real-world person images encountered retrieval tasks. To address this, we fine-tune the cross-attention layers of DiT 667 pairs:
using LoRA [9] on the dataset of person image-text
QK? Vd
Attention(Q. K. V) = Softmax
V,
K = WKT)x(TTw), V WVrtx((Tm))
(2)
where Q denotes DiT image features, Tu is the text encoder output, and W K. Wy are learnable projection matrices. During fine-tuning, only the LoRA components in the cross-attention layers updated, while all other parameters remain frozen. Given weight matrix W Raxi, LoRA introduces trainable matrices B E Rixr and A E RR'x, with r & miinh,,)) l), and computes the residual update AW- BABA, where controls LoRA strength and 7 is learnable layer-specific scaling factor. The updated weights are then given by W = W + AW, enabling purameter-efficicn adaptation. Training guided by mowthnn objective function 70).
Once fine-tuning is complete. as shown in Figure 3l the generative model's inherent consistency capability, that is, the ability to generate coherent elements within single image, is ingeniously leveraged to synthesize image pairs with consistent identities, which cannot be achieved through independent generation. Specifically, we first define a layout prefix and merge Tr, and Tr, into unified prompt. Then, this prompt is input to the model to generate a single image with left and right sub-images. The final images I, and I are obtained by cropping:
Rectangular grid layout for left and right images. Each image is independent, Left: Tí., Right: Ti,
Furthermore, to maximize textual quadruple utilization, we dynamically adjust 3, generating n image pairs for each textual pair (Tr,: Tr, ). thus creating 2n triplets. Besides, images within the same triplet share a unique ID, while those within groups that share the same relative captions are assigned common group ID (GID), facilitating label smoothing during training.
Data Filtering. To ensure the quality of generated data, the MLLM [10] is employed to evaluate each generated triplet on a scale from to 10 across four criteria: (1) naturalness of individuals in lg and 4, (excluding resolution and instead focusing visual realism, noise, and artifact presence); (2) identity consistency between I, and I;; (3) alignment between images and their corresponding descriptions (Iy ++ Ti,): ); and (4) CPR task relevance (Ig Ty - It). Triplets with average score below a strict threshold of 8.5 are discarded, leading to the removal of approximately 59% of the data.
Based on this pipeline, large-scale synthetic dataset named SynCPR is constructed, consisting of 1.15 million high-quality triplets. Further implementation details regarding data synthesis (e.g.. complete prompt templates and additional visualization examples) can be found in the Appendix
5

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3.2 End-to-End Composed Person Retrieval Framework
new retrieval framework is proposed achieve end-to-end CPR, where the FAFA is constructed to achieve fine-grained feature alignment.
3.2.1 The FAFA Architecture
Inspired by BLIP-2 38 the proposed FAFA architecture, as shown Figure 2b), integrates a frozen image encoder and lightweight Query Transformer (Q-Former). The Q-Former enables efficient multimodal representation extraction through trainable query mechanism. It supports two encoding pathways: one is an image-guided path that combines visual and textual inputs, and the other is purely visual path.
Given an input triplet Tq. It), the frozen image encoder extracts visual features from the reference image g. which are then combined with the relative caption T. and fed into the Q-Former. The textual [CLS] token, after passing through a text projection layer, yields the query representation fy R2 Meanwhile, the target image I is processed by the same frozen encoder, and its visual features are routed through the purely visual branch of the Q-Former. The learnable query tokens in this branch are projected along the sequence dimension using visual projection layer, generating the E R xd, where N denotes the number of learnable queries and is the feature dimension.
fine-grained feature representation ft
3.2.2 Fine-grained Adaptive Feature Alignment
Fine-grained feature matching is another inherent challenge in the CPR task. To deal with the issue, we propose a fine-grained dynamic alignment mechanism, integrating feature diversity supervision and masked feature reasoning into an end-to-end optimization strategy.
Fine-grained Dynamic Alignment (FDA). Unlike conventional contrastive learning methods 33 7 that focus on global single-feature matching, the proposed approach dynamically aligns multiple fine-grained features from the target image with the query representation. Specifically, for each input triplet, the similarity between the query representation fy and the set of target fine- tineggained features ft is calculated by using dynamic feature selection and aggregation:
Σ Topk, (1 (1IETT
ff AU) [IAlll IIAGII
\
N
Sim(fa. fc) k
(3)
where TopK, (-) denotes the 7th highest similarity score. This mechanism allows the model to adaptively select the most relevant fine-grained features for improved precision. During training. distribution matching and label smoothing are incorporated to enhance contextual alignment. For
Vi.i where yt.j = batch size B, the ground-truff -rrth matching probability defined as: Gi.j = Σ vi.k"
for exact matches (with the same ID), Vi.j a, (0, 1) for partial matches (with the same GID), and Vi.j = for unmatched pairs. The predicted distribution is normalized via softmax: Pi.j = exp(Sim((,,.
where T is temperature parameter. Then, the query-to-target alignment loss
E (x55
is defined as:
B
8 B >>>on p., log (-1
Eqze
Σ B
(L( 1
Pijj
(4)
G1. +
where KL(-I.) represents Bellback-Leibble divergence and ensures numerical stability. The reverse loss Lizy is computed analogously by interchanging f, and ft. leading to the overall alignment loss: Lida Lggs + Lezy-
Feature Diversity (FD) Supervision. To reduce redundancy, feature dispersion loss is introduced:
1
fe(i) J.() m, 0)
L,5 = N(N
max ff(()) -Ix())
(5)
where sets the maximum cosine similarity, encouraging diversity among internal representations.
Masked Feature Reasoning (MFR). To exploit complementary information between reference images and relative text, a bidirectional MFR strategy proposed. Specifically,, the random masking operation (30%) is applied to fa and the average pooled target image feature ft, thus producing

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Celeb-relD
PRCC
LAST
wearing a white plaid shirt the outside.
wearing a dark blue skirt.
wearing a yellow top dark blue pants.
Figure 4: Some representative examples from the ITCPR dataset.
masked features I, and Jt. The four features are jointly fed into lightweight decoder & minimize reconstruction loss:
Cun = - Eep [Ir, (Af.. - ADE
(6)
This loss drives the model recover complete representations and enhance modal alignment. Finally, by combining the above three components, the overall training objective is formulated as: Ljda Aa A2Cmfrr where λι and A2 balancing weights for the auxiliary loss terms.
3.2.3 Inference Workflow
During inference, the fine-grained feature sets for all target images in the retrieval dataset are pre- extracted and stored V f Given combined query feature fq. the similarity between it and each fi is computed using the same dynamic alignment method employed during training, thus ensuring efficient and reliable retrieval of the most relevant target images.
3.3 ITCPR Dataset
To objectively evaluate CPR methods, we manually construct the ITCPR dataset. Each triplet contains reference image and target image sharing the same identity, selected from public llthes--hanging
datasets including Celeb-reID [11. PRCC [3], and LAST [12], ensuring identity consistency despite variations in clothing or background. Each triplet also includes relative caption explicitly highlighting differences between the two images, requiring models to jointly leverage visual and textual information for accurate retrieval. To ensure evaluation reliability, gallery images carefully reviewed eliminate potential delse-negative cases. Ultimately, ITCPR contains 2.225 annotated triplets, comprising 2.202 unique query combinations (4,, T2) from 1,199 identities. The gallery consists of 20,510 person images, among which 2,225 correspond directly to queries. Representative examples are illustrated in Figure
Experiments
4.1 Experimental Setup
Datasets. For data generation, we fine-tune Flux.1 8 on the training split of CUHK-PEDES 4.
a widely-used real-world person dataset containing 68, 126 manually annotated image-tex: text pairs. For the CPR task, FAFA is trained on 1.15 million filtered high-quality triplets from SynCPR, and evaluations are conducted on the manually annotated ITCPR dataset. Detailed descriptions of all datasets are provided the Appendix
Choice of Dataset for Fine-tuning. For fine-tuning the model, we chose CUHK-PEDES over other datasets such as UFine6926 (72). ICFG-PEDES [33, and RSTPReid (74). CUHK-PEDES was selected primarily because of its greater scene diversity, encompassing images from five surveillance datasets that represent wide range of real-world scenarios, including urban environments and public spaces. This diversity is crucial for ensuring that the model generalizes well across various contexts, which is essential for practical person retrieval tasks. contrast, ICFG-PEDES and RSTPReid, which mainly focus on constrained environments like parking lots (MSMT17 75)). lack the same level of visual variation, potentially limiting the model's adaptability to real-world scenarios. Furthermore, while UFine6926 offers more fine-grained text-image pairings, its higher image quality and controlled video sources do not provide the same environmental diversity as KUHK-PEDES, which could lead to overfitting, By fine-tuning on CUHK-PEDES. strike balance between rich textual descriptions
7

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Table I: Comparison of methods across different domains and settings. For all domains other than CPR, models are trained on the most representative dataset within each domain.
Domain
Method
Ref.
Pretraining Data
Setting
Rank-1
Rank-5
Rank-10 10
mAP
TransReID
76
ICCV21
7.27
17.30
22.75
12.57
IPR
SOLIDER
78
CVPR23
Market- 1501
?
Image-only
8.45
18.48
23.89
13.74
ReID
79
AAA123
7.95
18.12
22.75
13.31
CAL
80
CVPR22
9.86
22.34
29.20
16.45
CC-IPR
LTCC
Image-only
FIRe2
TIFS24
10.76
22.84
29.29
17.00
RaSa
81
LICA123
28.02
49.23
57.77
38.04
CUHK-PEDES 4
Text-only
IRRA
CVPR23
26.39
46.46
56.27
36.13
TPR
Image-only
6.31
13.78
18.46
10.43
RDE 84
CVPR24
CUHK-PEDES 4
Text-only
26.43
47.41
56.45
36.35
Image Text
29.79
51.82
60.49
40.10
SOLIDER + RaSa
30.97
52.86
61.81
41.22
Fuse
Image Tex
FIRe2 RaSa
32.89
54.27
62.03
42.16
Pic2Word 49
CVPR23
CC3M 85
21.21
37.15
44.51
29.11
ZSCIR
CoVR-BLIT 86
AAA124
WebVid-CoVF 86
Combination
26.75
47.68
56.36
36.49
LinCIR (VTT-G) 87
CVPR24
23.93
44.46
53.18
33,95
CIRR
24.02
44.64
53.45
34.08
CaLa 47
SIGIR24
Combination
SynCPR (Ours)
39.33
60.85
68.66
49.29
CIR
CIRR 6
25.07
45.73
54.50
35.05
SPRC
ICLR24
Combination
SynCPR (Ours)
42.27
61.81
69.35
51.62
CPR
FAFA (Ours)
SynCPR (Ours)
Combination
46.54
66.21
73.12
55.60
" Bold indicates the best performance; Underline indicates the second best.
and diverse visual settings, ensuring the model's robustness when confronted with the variety of challenges encountered in real-world person retrieval tasks.
Implementation Details. All experiments are conducted using two H800 GPUs. During the SynCPR construction process, we adopt Qwen2.5-70B 77) the LLM to generate textual quadruples, and use Flux.1 3 as the base image generation model. This model is fine-tuned by LoRA (9] with its rank r 64. and set 8 1 to generate five identity-consistent image pairs per quadruple in the most realistic style. Another five image pairs are generated using random values of (0. 1) to ensure stylistic diversity. Qwen2.5VL-32B [10] is employed for data filtering. For training the FAFA framework, we set the total number of epochs to 10 and use a batch size of 256. The soft label strength in FDA is set = 0.02. The margin parameter m. in Lya is set to 0.5. The loss balancing hyperparameters are A₁ 1 and A2 = 0.5. All comparison methods reported by them. Additional implementation details can be found in the Appendix C.
0.5, the number of selected fine-grained features is k 6, and
implemented using the optimal settings
Evaluation Metrics. Retrieval performance is measured using Rank-k k accuracy and mean average precision (mAP). Rank-k indicates the probability of correct matches in top-k retrievals, while mAP averages precision across all queries.
4.2 Results
To objectively evaluate FAFA and the SynCPR dataset, we extensively compare recent approaches from person retrieval and composed image retrieval. The compared methods are categorized into four settings based on imput types: 1) Image-only, which relies solely on the reference image and retrieves targets via the visual encoder; 2) Text-only, which uses only relative captions and retrieves targets through cross- modal alignment; 3) Image + Text, which calculates similarity scores separately via the first two methods and then retrieves targets using their average; and 4) Combination, which simultaneously inputs both reference image and relative caption into the model for target retrieval. As shown Table our method consistently outperforms others across all settings. Specifically, directly applying IPR methods yields the lowest performance due to clothing variations between reference and target images. Even CC-IPR methods trained explicitly on clothes-changing datasets struggle due to limited generalization. In contrast, TPR methods achieve relatively better results, as

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Table 2: Ablation experiments on each component of FAFA. To validate the effectiveness of FDA, we additionally introduce the image-text contrastive loss (ITC) for comparison.
Components
ITCPR Dataset
No.
SynCPR
ITC
FDA
FD
MFR
Rank-1 1
Rank-5
Rank-10
mAP
1
41.33
61.72
68.94
50.94
2
V
45.04
64.90
72.21
54.41
46.05
65.85
73.02
55.49
45.78
65.58
72.62
55.13
5
46.54
66.21
73.12
55.60
Redel
MAP
01ak-1 150
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Figure Sensitivity analysis of FAFA on hyperparameters and analysis of the SynCPR dataset.
the relative captions inherently match target images, although some visual information is missing, Among baseline approaches excluding our method. the Image Text strategy achieves the best results, validating the rationality of our ITCPR dataset and emphasizing the necessity of combining visual and textual queries for optimal retrieval.
For CIR methods, although inherently designed for joint image-text queries, their training generally targets natural images involving significant visual modifications, thus lacking fine-graie one-grained retrieval capability required by CPR tasks. Notably, supervised CIR methods trained on original CIR datasets perform worse than certain ZSCIR methods on our task, underscoring the need for CPR-specific datasets and methods. Training supervised CIR methods on our SynCPR dataset significantly improves retrieval performance to practical level. Furthermore, integrating our fine-gre aeee-graiedd retrieval framework FAFA with SynCPR further substantially enhances retrieval accuracy, confirming the indispensable roles of both the proposed dataset and FAFA.
4.3 Ablation Study
In this section, we conduct comprehensive ablation experiments investigate contribution each component within the FAFA framework. Additionally, we discuss the impact of key hyperparameters both the FAFA model and the data generation process.
FAFA Model. We train variants of the FAFA model with different components on the SynCPR dataset and evaluate their performance on the ITCPR test set. As shown Table experimental results demonstrate that due to the specific nature of the CPR task, employing our proposed fine- grained dynamic alignment strategy can substantially improve retrieval performance. Moreover, both supervision strategies, namely the FD strategy for enhancing feature diversity and the MFR strategy for capturing complementary features, contribute effectively to performance gains. The FAFA model equipped with all components achieves the best overall performance.
Hyperparameters of FAFA. Figures 5ja) and 5(bb illustrate the impact of critical hyperparam- eters in our proposed FAFA model, namely the soft label strength a and the number selected fine-grained features lo in FDA, on the retrieval performance. Regarding a, lower values mean that the triplets generated from the same textual data will be treated more negatively, thus have an adverse impact on FAFA's semantic understanding. Conversely, higher values will weaken FAFA's ability to maintain identity consistency. This observation is consistent with our experimental results: as a increases, the retrieval performance initially improves and subsequently declines, achieving optimal performance when a = 0.5. Similarly, the number of fine-gr fine-grained features also exhibits comparable trend, and the optimal performance can be obtained when A = 6. This also aligns with expectations, because smaller & values will restrict the involvement of sufficient fine-grained features in retrieval, whereas excessively large k values may make the training process too homogenized, thus not suitable for retrieval tasks that require distinctive feature representations.

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SynCPR Dataset. Figure ((c) presents the influence of applying various scoring thresholds on retrieval performance and data filtration ratio after generating all triplet data. Without any filtering, the potential noise in the dataset negatively impacts the FAFA training process, and consequently reduces retrieval performance. The optimal retrieval performance is observed when the threshold is set at 8.25 and 8.5. To enhance training efficiency and ensure the high quality of the SynCPR dataset, we finally adopt the latter. Furthermore, we perform sampling on the retained 1.15 million high-quality triplets via GID to validate the appropriate scale of the SynCPR dataset. As shown in Figure as the dataset size increases, the retrieval performance improves rapidly. When the number of samples exceeds 500k, the marginal gains gradually diminish, and it saturates when the number of samples reaches approximately 800k. This confirms that our SynCPR dataset containing 1.15 million triplets is large and challenging enough to train better CPR models and is also convenient for comparison with our baseline method.
Conclusion
We introduce practically significant task of composed person retrieval. Firstly, we put forward scalable synthetic pipeline to address the data scareity problem, and construct a high-quality SynCPR dataset at million scale. Secondly, novel FAFA framework is introduced to enhance fine-grained retrieval accuracy. Extensive experiments on the newly annotated ITCPR benchmark confirm the significant superiority of our approach over the existing IPR, TPR, and CIR methods. Future work will explore composed person retrieval based on multiple images and multiple textual descriptions, as well as retrieval under open-set conditions.
Ethical Considerations. While the CPR task holds significant promise for applications such as locating missing individuals, it also raises critical ethical concerns, particularly regarding privacy and the potential for surveillance misuse. The ability to track individuals across different locations introduces privacy risks, which can be mitigated through various safeguards. For instance, invisible digital watermarks have been embedded in the images of the generated SynCPR dataset to ensure traceability, and access is restricted to academic use under responsible-use agreements. Additionally, biases inherent in synthetic data generated by LLMs have been addressed by ensuring substantial diversity in the generated data. Statistical information and visual representations in Appendix B.2 of the appendix effectively demonstrate the demographic diversity of the SynCPR dataset, encompassing various genders, ages, and ethnicities. These measures ensure the responsible use of the proposed method, adhering to the principle of "Tech for Good" while addressing potential societal risks.
Limitations
While the proposed CPR task demonstrates significant potential, several limitations remain. The SynCPR dataset, although highly diverse, relies on synthetic data, which may still introduce a domain gap when applied to real-world scenarios. Despite efforts to minimize this gap through adjustments in the generation strategy and fine-tuning of the generative model, the synthetic nature of the training data may not fully capture all the variations found in real-world images. Additionally, the ITCPR test set primarily focuses on clothing changes, which limits the model's 's ability to generalize to other types of variations, such as changes scenes or hairstyles. Expanding the dataset to encompass a broader range of person-related variations will be an important area for future improvement. Furthermore, the current FAFA framework and the ITCPR test set focus mainly on scenarios where only a single image and textual description are provided, which may not align with more complex real-world situations. Addressing this limitation will be a key focus for future work.
Acknowledgements
This work was supported by the Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing (GJJ-24-021) and the BUPT Innovation and Entrepreneurship Support Program (2025-YC-T043..
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Appendix
A
More Details for High-Quality Triplet Synthesis
The triplet data required for Composed Person Retrieval (CPR) consists of three key elements: reference image Lg, relative caption Tg and a target image 4. Two significant challenges hinder the complete synthetic generation of such triplets. Firstly, generating pair of person images (1,, 4e) consistent with real-world distributions while preserving identity. Secondly, providing accurate textual descriptions of relative changes between the two images. To address these challenges, as shown in Figure S6] we effectively divide the data synthesis process into three steps. First, a Large Language Model (LLM) (7] generates textual data comprising descriptions for synthesizing image pairs and relative captions. Second, generative models [8] utilize the textual descriptions to produce realistic and identity-consistent image pairs, thereby forming the required triplets. To ensure realism in the generated images, we further fine-tune the generative models. Finally, a multimodal large language model (MLLM) [10] evaluates the synthesized triplets across multiple dimensions, filtering out lower-quality data.
A.1 Diverse Textual Quadruples Generation
In this step, we simplify the multimodal triplet generation objective (T Tg. Ix) to purely textual quadruple generation (Ti,: Tg-st, Tz-sq: Tr, using an LLM. Each quadruple comprises a reference description Ti, a target description TT, relative caption describing changes from Tr, to Ty, (T (c) and another describing the reverse changes (Ti+g). To achieve this, we select QWen2.5-72D 77 as the LLM and carefully design structured instructions to generate quadruples meeting the desired criteria.
The instruction format, illustrated in Figure So initially provides an overview of the task and fundamental requirements for the LLM. It then specifies detailed guidelines for generating each element within the quadruple. Additionally, the instructions include three high-quality example outputs randomly selected from 100 manually annotated cases to enhance the quality and stability of the LLM outputs. Random sampling of examples promotes diversity in instructions, preventing repetitive outputs caused by similar inputs.
Notably, the instructions suggest the primary character, clothing items, and colors, randomly drawn from candidate lists. These lists are derived from relevant datasets 4 73 74 descriptions, supplemented by additional related elements. Such design ensures generated data closely aligns with real-world distributions while maximizing its diversity and comprehensiveness. The LLM subsequently generates structured prompts for image synthesis and composed retrieval based on these provided elements.
containing person
A.2 Identity-consistent High-quality Image Synthesis
Once the textual quadruples (T',, Ta-st, Ti- Ti are obtained, we convert Ti, and TT, into their corresponding images, Ig and Jt, thus forming the desired triplet data. For this purpose, we adopt FLUX.1 [8. an advanced generative model, as the base model and fine-tune it using Low-Rand Rank Adaptation (LoRA) [9 on a text-based person retrieval (TPR) dataset 4 to generate person images consistent with real-world distributions. Due to the inherent randomness in diffusion model image generation, a critical challenge remains ensuring identity consistency between paired images. How- ever, diffusion models intrinsically possess the capability to generate two identical or similar objects within one image. We leverage this internal consistency capability by generating two sub-images within single image, thereby ensuring detailed consistency of shared elements in ln and 4, To achieve this, we specifically design the image generation prompt template to include two equally sized sub-images with the same identity.
During this stage, prompts generated by the LLM are input into FLUX.1 1 to produce images with a resolution of 400 x 400. This configuration allows each generated image to be split into two sub- images (192 x 384), forming the pair (T,, It). This padding strategy mitigates inaceuracies that may arise during image generation and avoids artifacts caused by image cropping. As depicted in Figure S6 this method effectively maintains identity consistency between the sub-images while varying their appearances and states, demonstrating clear advantages over separate generation. Consequently,
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Character List
Color List
Clothes List
wan, woman, boy, girl, tonnager elderly man, eiderly woman. shild, baby, tuddier, young odult. widdle-aged man, middie-aged student, teachor, docior, nurse. chef, engineer, office worker, police officer, firefightrr farmer, artist, musician, athlete. comstraction sion worker, salesperion. scientiot, pilot, driver, horisto. fowrist, shopper oyclist, jogeer, hiker, swimmer, dancer, yoga pr practitioner, gardener. photographer, moveler, waitress, street veador, burincsiman. shadent with backpack, person in wheelchatr, stblings, bride, growm, bride with wedding drem. groom in person with per dog, person holding umhreila. person wearing headpkoner. willtury officer. paramedic..
rol, green tlut yellow, eyan, wagenia, black, white, gray, brown, orange. purple, pink, beige. svory, say, teal, murson, olive. lime, gold, silver, brouse, amber, peach, turquoise, lavender, coral, indign plun, salmon sist khaki, chocolate. violet, emeraid, jade, aquamarine, cream, lon, Burgundy, scarfet, chartreuee, cobalt, pertuinble. ruby, sapphire, amethyss, topaz, fuchzia, blush, caeary. copper, denim, chidd pearl, sage. senfoeu, sepiu. state, tongrrte, ultromarine, vermiltion, wine, foreet green. sky blue, ocnon blue. sand, desert, sunset orange, midniggt blue, stone gray, grare green, cloud white, seaweed green, lavender fleld, mountain gray, rose goid. platinum, steel, oaya, diamind, ruby red, courrldd groen. sapphire blue, ametkyst purple, opal, topaz yollow, ash, pester, slate gray. grophiie, smoke, dove ge sonee toupe, off-whire, eggihell noon green, pink, neom yellow, orange, eleciri
Tishirt, shirs, blouse, polo shirt, long-steave shirt, cordigan, crop top sweatsbirt, vest, jeuns. sharto, skirt, cargo pants, chinos, denim skirt, mini shirt. pleated skirt, cargo shorts, jacket, coat. blaser, windbrenker, parku, trouch leather jacket, donim jacket, bomber jucket, putfer jacket, raincoat, snockers, soudiass. tonfors, high beels, flate, uxford shoes, ranning shoes, liking boots, alip stoes, espadrilles, ankle boote, glasser. sunglasses, scarf, baseball cap. boante, backpack handbaag belt, watck nechlace. currings. bracelet, gloves, kairpin, bow tie, umbrella, headphoner, fauny pack, dress, suit, susedo, evening gown, sportswear, yoga pants, rwinssutt, bikinl. apron, uniforn, coot, chef's hat, striped pattern foorl print, pland shirt, polla dots. plain color, graphic print, checkered design. rollesiap sleeves, belled waist.
koodle, tank top.
legsings, rucoigunts,
charconl.
brown.
pastel pink. passel yellow, passel green, buby blue. baby pink, fluwrssont eecent green, floressent orange. royal blue, mustard, apricor. ceralean, persinmon, maseve. oclor, chony, ebony black, jude green, carnation pink, raspberry, peacock blue. mandarin. brick rod, bubblegun pink.
hot pink, pastel
Random
Random
Random
Please help me provide a prompi with the same structure my ctample but different content. Structurally, first provide two detailed descviptions the appourance of single person's image, labeled Prompil and Prompt2, Ensure both describe the person with partially different outfis bbackrroudsllode choose omit relevant parts when descrihing). When Prompt) given, Relotive Prompt] should he relotive description abhresiated of Prompt2, only montioning ougft difforences from Prompt] (omit ideutical elemenas). Sinslorly, Relative Prompt2 should deserthe only the differences between Prompt] and Prompt2, Three exomples: [Examples) Note: The aniput must descvihe '[Charadeerj' least one prompt must mention '[Clothes]', and include at least one '[Calorj' clothinglacossso laccessoorr..rrr.............ose The final output must be logical, strietly follow the example structure, avoid duplicating any example's
may vary, and note that backgroundslastoon
are relatively less importont compared appearance, to you con
all requirements above, and sentence longths similar the cxamples.
LLM Instructions
Step 1: Diversified Quadruple Generation
Qwen2.5
Exomples
LLM Output
Promptt (Ti, J:4 womson with Mark hair is wearing a teal tunk: top. black shorts and whilee sundals, She is corrylng o black crnsebodly bog
Prompt2 (Ti, J:A woman with black hair wearing teal tank top. black shorts, and black anhle boots. She carrying a white tote bag.
Relative Prompt] (T ): Wearing black anble booss, corrytng white tote hag.
Relative Prompe2 (T, J: Wearing white sandais, carrying black croushodl bog
Rectangular grid layout for left and right images. Each image independent, show photoroalistic style. Left: T Right: T.-.
Generation Prompt
Step 2: Constant Image pair Synthesis
Dataset
Training
Inference
Wariin black boots, corrinn tolk bay
CUHK-PEDES
Diffusion
Weining white sundals, corrying a black creasbody boo
Model
Step Data Filtering
MLLM Instructions
Exulmate wen symthetic images gooriily survellance-ttle; ignore image sharpness) depicting the name person with differeet owfite haved foar criteria (1-10 scale). Backgrann:: Both images are generated corresponding prompts and munual modifficatin description. Evalutte these two images ncross four dimensions (1: scate, 1-warst, 10 bo-besy, First dimotaion 'Qudityy: Arsess if both images depict plassible human figures without border artifacts or incoherent compositions, lgnoring sharpness. Higher scores indicane natural, well composed figures. Second dincusion "C "Consistong": Evaluate if and 1, represent the same person dospite differing pufftts/ooer/ssccnns Higher scores mean stronger factal/body feature consistency. Third dimcosion 'Align': Check hon accurately matcher T1 and I matches Tt, verifring objects, colors, and scene clements. Higher scares reflect precise test image aligament Fourth dimensiion 'Relative prompt quality: Judgs if T accurately wodifies I juto I, and and whether Tt accurasely reverses this transformation from I, to Dedict points for incorectinnecssse essory changes. Higher scores mean prompte precisely capture matual differencer while preserving shared elements. Output format: 'Dualitys i score, "Comststoncy's 'Aliga' score. 'Relative Relathee_rompp prompt qualityy Use singie quotes. Only provide the dictiowary, no explonations.
Qwen2.5-VL
Scores
Quality: 9. Consisiincy::
Algnn 10.
Relutive_prompe Bactol__rooooropr_e quality:
SynCPR
Figure S6: Pipeline of high-quality CPR triplet construction with detailed instruction design.
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Low-Qual Low-Quality Person Images
Identity Inconsistency
Text- Image Misalignment
Low- Now-Quality Relative Caption
man is jopping outdoors on a paved path, wearing black sip-up jacker over a bright yellow shirt, paired with blue ports
heodie, resting bicycle.
sneekers.
black ziprup
standing frent of a bicycle.
The jooping setting. jocket.
dressed black athletic leggings with Nike logo, and wears neon green rurning shees.
rurning
Wearing beige button-ug up shirt, sitting park
Representative examples of samples filtered out during the data filtering process. From left to right, each panel corresponds one of the four evaluation dimensions, and the samples excluded due to low scores in their respective dimensions.
Figure S7:
acquire the desired dataset, and swapping the reference and target images yields two sets of triplet annotations. By dynamically adjusting the LoRA strength 3, each textual quadruple generates ten image pairs. Images with identical relative captions are defined under the same group identity (GID), thus forming strong positive samples within triplets and weak positive samples within groups, with other instances treated negative samples.
A.3
Data Filtering
To further ensure the quality of the synthesized data, we employ Qwen2.5-VL. 32B 10 combined with carefully designed instructions to score and filter all generated data. Four dimensions are considered: first, assessing the fidelity of person images I, and L,, focusing on naturalness, noise, and artifacts while ignoring image clarity; second, evaluating identity consistency between I, and Ie; third, assessing the alignment between images and their descriptions (e.g., I <> T fourth, evaluating the overall quality of the triplet (7, + +T, -> I,), where higher scores indicate the ability to accurately infer I; from the combination of L, and T. with minimal overlap and high complementarity. Qwen2.5-VL rates each dimension from to 10, and the final score is the average of these dimensions. Triplets scoring higher than 8.5 are retained and included in the SynCPR dataset. Representative examples of discarded low-quality data are shown in Figure S7
B
Additional Datasets Details
B.1
ITCPR Dataset
In contrast to existing CIR datasets 5 66. where reference and target images only need to be loosely related, the CPR datasets subject to the constraint that both of them depict the same person. Therefore, when constructing the ITCPR dataset, we ask the selected images to have the same identity, but wear different clothes or be in different scenes. In our implementation, publicly available clothes- coothes-changing datasets such Celeb-reid [TT) PRCC [33] and LAST [2 are utilized as image sources.
B.1.1 Dataset Annotation Process
The annotation process, as shown in Figure S8 primarily consists of three steps. The first step involves selecting identities from the image data sources that have multiple images with different outfits, ensuring a diverse selection for subsequent steps. In the second step, pair of images associated with each chosen identity is selected and denoted as Ig and I, It is worth noting that, ideally, these two images should depict partially matching outfits, allowing Ig to provide additional information beyond facial features and body posture. This additional clothing information is not mentioned in the corresponding annotation Tg. ensuring that CPR methods can only correctly identify T, by utilizing both I and Tg. Once the image pair is selected, the process moves to the third step, where manual annotations are created to specifically capture the differences between Lg and I, For
booting-related
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1t Select Identity
Step2: Select
Step3: Annotate Relative Text
Image Pair
Wearing dark green sweater and no shoes.
lq
1
T q
2
Wearing pink top, ring, and her hair tied up
Clothes- Changing Datasets
Figure S8: The annotation process of the ITCPR dataset. The annotation process can be summarized in three steps: the first step is selecting identities from the dlothes-changhnn datasets, the second step is choosing pairs of reference and target images for each identity, and the third step is manually annotating the relative captions.
instance, as shown in case of Figure S8) if the skirt is the same in both the target image and the reference image, it does not need to be described; the annotation focuses only on differences in the top and shoes. After manually annotating Tg. complete triplet annotation process is finalized. Repeating this process, a batch of triplets (Lq, Tq. 4)) can be generated for testing CPR methods.
B.1.2 Re-Annotation of the ITCPR Dataset
The gallery contains a large number of noisy images, which may introduce false negatives. For example, for certain queries, some images may be potential ground truth but remain unlabeled. Including such cases would reduce the reliability of the evaluation metrics. To address this issue, all images in the gallery are screened, and any false negative images identified in the dataset are re-annotated. The ae-annttation process is illustrated in Figure S9 After completing the dataset annotation and adding noise images the gallery, we use well-trainee visual encoder (78 search for the most similar images for each target image, followed by manual inspection and verification. Through this approach, we effectively eliminate false negatives in the ITCPR dataset.
B.1.3 Statistics of the ITCPR Dataset
In summary, ITCPR comprises total of 2.225 annotated triplets. These triplets encompass 2,202 unique combinations Tg) as queries. ITCPR contains 1,151 images and 512 identities from Celeb-relD ITI 146 images and 146 identities from PRCC [3], and 905 images and 541 identities from LAST [1). In the target gallery, there are a total of 20,510 images of persons from the three datasets, with 2,225 corresponding ground truths for the queries. The textual annotations have an average sentence length of 9.54 words. The longest sentence contains 32 words, while the shortest sentence only contains words. These annotations are exclusively designated for testing in the 7S-CPR task, which expects to achieve substantial performance without utilizing any data from the three datasets mentioned above.
B.2 SynCPR Dataset
Using our proposed automated construction pipeline, we successfully build the SynCPR dataset, which is a fully synthetic dataset specifically designed for the composed person retrieval task. In the first stage, we utilize Qwen2.5-70B 7 to generate total of 140,500 textual quadruples. In the second stage, by employing fine-tuned LoRA [9] combined with Flux. 1 g and setting 3 = 1 for the most realistic person image style, we generate five image pairs per quadruple. Additionally, we create another five image pairs using randomly selected B E (0. 1), ensuring diverse styles across generated images. Combining these images with two relative captions from each quadruple yields a total of 2,8 1,,00 valid triplets. In the third stage, under stringent data filtering criteria, 1,153,220 high-quality triplets are retained. Among the retained samples, a total 177,530 unique GIDs are involved. The average length of the relative caption sentences is 13.3 words, excluding punctuation.
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Method
Target Image
Visual Token
Top-k Images
Visual
Encoder
Re-annotation
Gallery Images
Target Image
Re-annotation
Target Image
Re-annotation
Example
Figure S9: False negative elimination scheme in ITCPR. Top: The method of eliminating false negative images and adding annotations in the dataset. Bottom: Examples of false negative images re-annotated ITCPR.
Image;
Relative Caption Imagez
Image; Relative Caption Imagez
Image;
Relative Caption Image:
Weoring blook leggings, stonding next to a bike.
Wearing green Sirting swing.
Wearing gold Top. through park.
Weaning denim skirt. sifting on pork
Weering colored t-shirt, playing backyard.
Weoning derim jocket, gold chain bench.
Weaning a red striped F-shiri, carry rying
Weoning white cordigon with blarkeet
Wevring gray shcheboard.
pastel
bag
No hoodie, sing o bench.
Wearing white shirt, corrying a small block backpack.
ploid condipon dork grey
thermals and lyrng on
Weoning a wide- jut sunglasses, browsing map.
Waoning oyan jocket, shoes.
Weoring orange flip-
flays holding
sandy beach.
Taking photo of kndmmrkk sunglosses.
Weoning plaid shirt, cyan
Wearing white Steakers and corrying beardwilk.
Weaning mopento holding newspoper.
Weaning denim basket, welking through c garden.
Wearing burgundy
loafeers
Weening mogento sweater: holding
Reading a baok under jocket.
Wearing nowy candigen and block park.
Figure S10: Representative examples of samples from the SynCPR dataset.
In total, 4,370 distinct words appear across all sentences, further highlighting the diversity of the SynCPR dataset.
The samples from SynCPR dataset are visualized in Figure STO Thanks to our diversified textual
generation strategy, realism-oriented fine-tuning of generative models, and rigorous data filtering
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mechanisms, the SynCPR dataset ensures high quality, realism, and diversity of person images. By leveraging varied image generation prompts and the zero-shot generation capability of generative models, the SynCPR dataset encompasses rich scenarios, broad age coverage, diverse image clarity, varied attire and states of individuals, and comprehensive ethnic representation. Furthermore, the gender ratio in the generated dataset is highly balanced, with males accounting for 51.2%. Although SynCPR is entirely synthetic, its comprehensiveness significantly surpasses other manually annotated datasets within the person retrieval domain.
B.3 CUHK-PEDES Dataset
4 is a widely used benchmark for text-to-person retrieval, comprising 40,206 pedestrian images and 80,412 corresponding textual descriptions annotated across 13,003 unique identities. The dataset is divided into three subsets: a training set containing 34,054 images and 68, 108 descriptions for 1 11,003 identities, a validation set with 3,078 images and 6,158 descriptions for 1, 1,000 identities, and test set comprising 3,074 images and 6, 156 descriptions for a separate set of 1,000 identities. Each image is paired with two independent human-written descriptions, and the average length of the descriptions exceeds 23 words.
CUHK-PEDES
C Additional Experiments and Results
C.1 Additional Implementation Details.
All experiments are conducted on two NVIDIA H800 GPUs. For training the generative models, we select FLUX.I-dev [8] as the base model and apply LoRA with a rank of r = 64 specifically to the cross cooss-attention layers. The training is performed with a per-GPU batch size of 1, utilizing gradient accumulation over eight steps, and optimized using AdamW [88] with an initial learning rate of 1 x 10 10-step warm-up, and weight decay of 0.01, for a total of 20,000 steps. The training employs 6 mixed precision. Input person images from SEEHES are resized 192 x 384 during training. During inference, each prompt dynamically adjusts LoRA strength, generating ten paired sub-images of size 400 x 400 via 25-step beta noise reduction. Each generated image is first centrally cropped horizontally into two separate images, then each resulting sub-image is further centrally cropped to form a pair of person images sized 192 x 384.
For the Fine- -ane-grained Adaptive Feature Aligmment (FAFA) framework, we use BLIP-2 38 and a frozen ViT-G/14 [89] with an input resolution of 224 pixels. Input images undergo random horizontal flipping, random cropping with padding, and random erasing, followed by scaling the longer side to 224 pixels while preserving the aspect ratio. The images are then symmetrically padded horizontally to a final size of 224 x 224 before being input into FAFA. The model is trained on the SynCPR dataset using single NVIDIA H800 GPU with batch size of 256 for 10 epochs. Optimization is performed using Adam W (8 with an initial learning rate of 2 x 10-6 In FAFA, the soft label strength parameter set to 0.5, the number of selected fine- grained features is set 6, and the temperature parameter is 0.02. The margin parameter m in the feature difference loss L is 0.5. The loss balancing hyperparameters are set as A = 1 and λ2 = 0.5. Additionally, for subsequent training from scratch on the Composed Image Retrieval (CIR) dataset CIRR (6, the model is trained for 50 epochs with an initial learning rate of 10 10-5, while all other settings remain consistent.
In our experiments on CIRR, Rank-K K serves as the primary metric, measuring the likelihood of finding the target image within the top-K retrieved candidates. For CIRR, we additionally report Rank, K on visually similar subsets, with overall performance summarized as Aug. Rank-544Rnk..-1
C.2
Additional Quantitative Results
C.2.1 FAFA for Composed Image Retrieval
The CPR task can be viewed as a more constrained and finer-grained variant of the CIR task, involving stricter alignment requirements between the reference and target images. Consequently. the FAFA framework, originally designed for CPR, can naturally be applied to CIR scenarios. We thus conduct experiments on CIRR, the most representative dataset within the CIR domain. The results, summarized in Table RI, demonstrate that our FAFA framework achieves comprehensive state-of-the-ee performance with significant advantages, even in the context of CIR. Specifically,
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Table S3: Performance comparison with existing supervised CIR methods CIRR dataset only. The best results are marked in bold, and the second results are underlined. f indicates that the method is pretrained on its constructed triplet dataset.
Rank-K
Rank,-K
Method
Ref.
Avg.
K=1
K=5
K=10
K=1
K=3
TIRG
90
CVPRI9
14.61
48.37
64.08
22.67
65.14
35.52
CIRPLANT 66
ICCV21
19.55
52.55
68.39
39.20
79.49
45.88
ARTEMIS 41
ICLR22
16.96
46.10
61.31
39.99
75.67
43.05
CLIP4CIR
42
CVPR22
38.53
69.98
81.86
68.19
94.17
69.09
TG-CIR 9
MM23
45.25
78.29
87.16
72.84
95.13
75.57
BLIP4CIR+BI
4%
WACV24
40.15
73.08
83.88
72.10
95.93
72.59
CASE 92
AAA124
48.68
79.98
88.51
76.39
95.86
78.19
CoVR-BLIP 86
AAAI24
49.69
78.60
86.77
75.01
93.16
76.81
CompoDift
93
TMLR24
32.39
57.61
77.25
67.88
94.07
62.75
CaLa
SIGIR24
49.11
81.21
89.59
76.27
78.74
SPRC
ICLR24
51.96
82.12
89.74
80.65
96.60
81.39
FAFA (Ours)
54.48
84.07
91.48
81.05
97.11
82.56
Prompti ( (Ti, ): boy with blonde hair is wearing khaki parka with fur-lined hood, paired with dark blue jeans and brown hiking boots. He walking snowy forest. Prompt2 ( (T, ): A boy with blonde hair wearing a khaki parka with fur-fined hood, but this time it's paired with a red scarf, black ponts, black snow boots. building snowman.
β
0.1
0.2
0.4
0.5
0.7
0.9
B
0.3
B 0.7
β ß=1
Prompt1 ( (Ti, ): beach chair under parasol.
adult with blonde hair is wearing jade green tank top, denim shorts, and brown sandals. She is
lounging
Prompt2 ( (T, ): A young adult with blonde hair is wearing jade green scarf over white T-shirt, denim shorts, and brown sandals. She is walking along path.
0.1
0,2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
B
0.3
0.7
B
Figure S11: Person image generation results under different LoRA strengths.
FAFA outperforms the second-ranked method SPRC, which also utilizes BLIP-2 its backbone, by 2.51% % in Rank-1 accuracy on the CIRR dataset. When compared with Cal.a, another BLII-2-bbaed method, FAFA achieves even more notable improvement, surpassing it by 5.37% in Rank-1.
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Results
Image-only (SOLIDER)
Query
Wearing a brown plaid shirt, black leather shoes, another dark gray T-shirt, another blue jeans.
Text-only
(RaSa)
Image Text
(FIRe2+ RaSa)
Ours (FAFA)
Results
Image-only (SOLIDER)
Query
Text-only (RaSa)
Wearing white long windbreaker, white pants, gray shoes, and gray hat.
Image+ Text (aSa)
Ours (FAFA)
Figure S12: Comparative visualization of Top-10 retrieval results across different methods on the
ITCPR dataset.
C.3
Additional Qualitative Results
C.3.1 Effects of Different LoRA Strengths Person Image Generation
During the person image generation process, in order to achieve more comprehensive and realistic styles, multiple groups of images generated for each textual prompt by dynamically adjusting the LoRA strength 3 E (0, 1]. Figure S11 iillustrates the effects of different LoRA strengths on generated person images, clearly demonstrating that the same textual input combined with varying values of can yield distinct image styles. Specifically, when / = 0, the pre-trained generative model is employed directly, resulting in high-quality images but with noticeable discrepancies from real- world styles. As the 3 value increases gradually, the realism of the generated images correspondingly improves, ultimately reaching a style closely aligned with that of real-world person retrieval datasets at 3 =
C.3.2 Visualization of Results from Different Methods
Figure S12 presents two illustrative examples of the Top-10 retrieval results obtained by various representative methods from Table on the ITCPR dataset. It is evident that the Image-only retrieval method yields the poorest performance, primarily because it tends retrieve images with visually similar pixel distributions. Given that the dataset contains numerous instances involving clothing changes, this leads to suboptimal performance. Text-only retrieval also falls short of expectations, as most annotations in the dataset provide brief descriptions of clothing differences between the reference and target images, while the retrieval database includes many images with similar clothing. Combining both modalities typically retrieves the target image within the Top-10 results; however, its inability to dynamically complement multimodal query information leads scenarios where an excessively high match in one modality adversely affects the final retrieval results. For instance, in

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(Rank-1 46.54)
47.0
56.0
Runk-1
47.0
46.5
mAP
55.5
26.5
46.0
46.0
55.0
45.5
Rank-1
54.5
45.0
54.0
44.5
0.0
0.6
44.0
53.5
1.0
0.4
Ag
1.5
2.0
2.5
3.0
0.0
0.2
43.5
Mask Ratio (%)
60
100
53.0
Figure S13: Left: Variations in FAFA's Rank-1 performance under different balancing weights of auxiliary loss terms. Right: Relationship between FAFA's performance and the feature mask ratio in Lmfr-
Example ② of Figure ST2 the high visual similarity causes the Image + Text method's most confident retrieval results to closely resemble those of the Image-only method. In contrast, our proposed FAFA method dynamically extracts complementary information from multimodal queries, consistently identifying the target person's 's image among the top-ranked retrieval results.
C.4 Additional Ablation Study
C.4.1 Balancing Weights of Auxiliary Loss Terms
To fully leverage the synergistic effects of the proposed loss functions, extensive experiments on balancing the weights of FAFA losses are conducted. As illustrated in Figure S13, when fixing the value of X2, the retrieval performance of FAFA initially rises and subsequently declines with increasing Å1, achieving its highest performance at λ = 1. Similarly, fixing è and varying X2 reveals the same trend, ultimately attaining the optimal performance at X 1 and )2 0.5, which corresponds to our final selected configuration.
C.4.2 Feature Masking Ratio Lmfr
As demonstrated Figure S13 the optimal performance of FAFA in the Lmfr setting is achieved when the feature masking ratio is set to 30%. When the masking ratio is set to 0, it is equivalent to disabling the Lm/rr loss. As masking ratio increases, performance first improves and then declines. When the masking ratio exceeds 50%, the complexity of masked feature reasoning becomes excessively high, resulting clevated loss values that negatively impact overall training stability, thereby diminishing the effectiveness of the Lmafr component.
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NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
The main claims made in the abstract and introduction accurately reflect the paper's core contributions. Specifically, the abstract clearly introduces the novel Composed Person Retrieval (CPR) task, the automated synthetic data generation pipeline, the con- struction of the SynCPR dataset, the proposal of the FAFA framework, and the ITCPR benchmark. These claims are further expanded in the introduction with detailed descrip- tions of the task formulation, methodology, and motivation. All major contributions are consistently described and substantiated throughout the paper.
Justification:
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made the paper.
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Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
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Reviews:
[{"review_id": "0NcKwCG6J7", "reviewer": "Reviewer_iMvu", "summary": "The paper proposes a novel task, Composed Person Retrieval, which integrates visual and textual information to enhance person retrieval accuracy.", "questions": "- Could the authors provide a detailed analysis of the computational complexity and resource requirements for the triplet synthesis pipeline, particularly regarding LLM and diffusion model inference times, to assess its practicality for large-scale use?\n- Can the authors clarify how the FAFA framework handles extreme cases, such as significant occlusions or low-quality reference images, and provide corresponding experimental results to demonstrate robustness?\n- Could the authors discuss the potential for extending the CPR task to include multiple reference images or textual descriptions, as mentioned in the conclusion, and provide preliminary results or insights?", "limitations": "Please find the above section.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "- One weakness is the heavy reliance on the synthetic SynCPR dataset for training. While the generation process is impressive, there is no in-depth analysis of the domain gap between the synthetic training data and the real-world test data (ITCPR).\n- CPR task address the practical need to combine visual and textual queries for person retrieval, which is underexplored compared to image-based or text-based retrieval.\n- The paper is well-written and clearly structured, and The figures are effective at conveying the core ideas. The experiments and results are presented with clarity.\n- The proposed automatic data synthesis pipeline is scalable and innovative, generating a large-scale synthetic dataset to mitigate the lack of annotated data.\n- The originality of the FAFA framework is somewhat tempered by its reliance on existing architectures (e.g., BLIP-2), with incremental improvements over previous CIR methods.\n- Manually annotated ITCPR test set, containing 2,225 triplets, is relatively small, which might not be sufficient to draw robust conclusions about the generalization capabilities of the proposed methods.", "was_revised": true, "final_justification": "Thanks for your efforts in addressing my concerns. I believe the issues have been mostly well explained, and I will maintain my positive score.", "rebuttal": "We appreciate your efforts and time, as well as your overall positive assessment. In response to your crucial concerns, detailed clarifications are provided below.\n\n## Response to Weaknesses:\n\n**A1.1: In-depth analysis of the domain gap between synthetic training data and real-world test data**\n\nThank you for this insightful suggestion. For the Composed Person Retrieval (CPR) task, the textual data primarily describes relative appearance differences between the target and reference person images. Therefore, the main source of potential domain discrepancy originates from differences in visual distributions between synthetic and real-world images.\n\nDuring the construction of the SynCPR dataset, apart from knowing that the test images are real images, we have no prior access to any distributional information about the real-world test images. Consequently, we aim to ensure that the majority of images in the SynCPR are highly realistic and sufficiently diverse in distribution. Specifically, the image generator used in the SynCPR dataset is fine-tuned on the real-world surveillance dataset CUHK-PEDES. During inference, we adopt a mixed strategy: setting β=1 with a 50% probability to ensure highly realistic images, and randomly setting β with a 50% probability to encourage broad distributional diversity (partially overlapping with the real-world test domain). Its purpose is to minimize distributional differences in the absence of explicit test-domain information.\n\nTo quantitatively assess this strategy, we calculate the Fréchet Inception Distance (FID) using different settings. Specifically, we randomly sample 5,000 image groups (10,000 images in total) from SynCPR and compute the FID against all images annotated in the real-world ITCPR test set, using the same prompts but different β settings. The results are as follows:\n\n| Strategy | β=0    | β=1   | Ours (mixed) |\n| -------- | ------ | ----- | ------------ |\n| **FID**  | 134.85 | 96.69 | **89.24**    |\n\nThese results demonstrate that our current strategy effectively minimizes the domain gap between synthetic training images and real-world test images.\n\n**Theoretical justification**: Generally, when two distributions are narrow with minimal overlap, the FID value is relatively large. Conversely, when one distribution is broad (representing diverse SynCPR data) and the other is narrow (representing ITCPR data), with some overlap, the FID tends to be lower [1, 2].\n\n[1] GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. NIPS, 2017\n[2] Training Generative Adversarial Networks with Limited Data. NIPS, 2020\n\n**A1.5: Improvements of retrieval framework**\n\nIn the field of person retrieval, especially for the text-to-image retrieval task, it is a common practice to further fine-tune pre-trained Vision-Language Pretraining (VLP) models. Leveraging pre-trained knowledge enables rapid and effective alignment between textual descriptions and visual content, thereby achieving superior performance. Given that the CPR task also inherently involves cross-modal retrieval, we therefore select the visual encoder and Q-Former components of BLIP-2 as the foundational architecture. Importantly, we further propose three new modules to specifically address the fine-grained nature of person retrieval: Fine-grained Dynamic Alignment (FDA), adaptive Feature Diversity (FD) supervision, and bidirectional Masked Feature Reasoning (MFR), aiming to make full use of complementary information between reference images and relative captions. Comprehensive experiments and ablation studies verify the positive effect of each component on performance improvement. Although these improvements only constitute one aspect of our overall contributions, they have shown indispensable value in the context of our proposed retrieval framework.\n\n**A1.6: Scale of test dataset**\n\nIn the Text-based Person Retrieval (TPR) field, there are two widely-used benchmark datasets, namely the CUHK-PEDES and the RSTPReid. The former contains 3,074 images and 6,156 texts in its test set, while the latter only includes 1,000 images and 2,000 texts for testing. In contrast, our ITCPR test set contains 2,225 triplets (reference image, relative caption, target image) and requires more complex and meticulous annotations; thus, its scale is comparable to that of similar datasets and is capable of supporting the validation of model effectiveness. Considering that this is the first dataset in the field of CPR, the ITCPR can be expanded in terms of both scale and quality. Hence, we will follow your comment to continue with the annotation and expansion work in our subsequent steps while retaining the current version (so that other methods can conduct fair comparisons).\n\n\n## Responses to Questions:\n\n**A2.1 Computational Efficiency of Data Synthesis**\n\nWe conduct efficiency evaluations on a single H800 GPU following the configurations described in Section 4.1. The results show that: (1) With batch sizes set to 8, the average inference time for generating a single textual quadruple using Qwen2.5-72B is approximately 6.45 seconds. (2) The generation of a pair of person images takes around 3.23 seconds. (3) Filtering a generated image pair using Qwen2.5-VL-32B requires about 0.38 seconds. Therefore, synthesizing one annotated quadruplet, which consists of two triplet annotations, takes an average of 10.06 seconds, with each individual triplet annotation requiring 5.03 seconds. Consequently, approximately 17,000 annotated triplets can be generated daily on a single GPU, which is approximately 24 times faster than manual annotation (it takes an average of 120 seconds for one triplet), making our pipeline highly practical for large-scale applications. It is worth mentioning that this synthesis process only needs to be carried out once and does not require repeated computation. Furthermore, the SynCPR dataset will certainly be open-sourced, thus avoiding the need for redundant generation efforts in the future.\n\n**A2.2 Robustness of FAFA Framework**\n\nAs detailed in Appendix C.1, we incorporate strong data augmentation techniques during FAFA training, including random horizontal flipping, random cropping and padding, and random erasing. These augmentations enhance the robustness of the model to severe occlusions and low-quality reference images. Specifically, we evaluate the FAFA model trained on the SynCPR dataset under various perturbations applied to the ITCPR dataset. These perturbations include random erasing of image regions (covering 5%-30% of the image area) to simulate severe occlusions, and applying Gaussian blurring with randomly chosen standard deviations (with σ from 0.1 to 3.0) to simulate low-quality images. The results are summarized below:\n\n| Method | Test Data | Rank-1 | Rank-5 | Rank-10 | mAP   |\n| - | - | - | - | - | - |\n| FAFA   | ITCPR | 46.54  | 66.21  | 73.12   | 55.60 |\n| FAFA   | + Gaussian Blur  | 46.19  | 65.62  | 72.66   | 55.12 |\n| FAFA   | + Random Erasing | 45.00  | 64.31  | 71.84   | 54.07 |\n| FAFA   | + Erasing + Blur | 44.10  | 64.08  | 71.48   | 53.39 |\n\nThe results indicate that our FAFA demonstrates strong robustness. It can maintain its effectiveness even under severe occlusion and low-quality conditions, and the performance degradation is within a controllable range. Additionally, in response to Reviewer ZBdY's comment A3.1, we further discuss the performance of FAFA when encountering modality deficiencies, and the favorable results also support the robustness of our method.\n\n**A2.3 Feasibility of Multi-image and Multi-text Input**\n\nThank you for keenly identifying this promising direction for future research, as we envisioned in the conclusion. However, it is difficult to provide quantitative results in a short time because the corresponding test sets require high-quality manual annotations, and there is currently a lack of publicly available datasets that support multiple reference images and textual descriptions.\n\nNevertheless, from the perspectives of dataset synthesis and methodological extensions, we have designed a feasible technical solution. Firstly, for data generation, the established SynCPR dataset can be used as training data for image generators, enabling the training of consistent generation or image-editing models [3,4]. Such methods are helpful for generating multiple diverse images of the same individual based on one reference image combined with varied textual descriptions. Then, the textual generation and filtering pipeline proposed in our paper can naturally extend to construct a dataset supporting multiple references for the CPR task.\n\nRegarding the training of the retrieval model, simple modifications can enable it to adapt to the situation of multiple images or texts. One straightforward approach is to increase the dimensionality of image input and use placeholders when necessary. Meanwhile, multiple textual descriptions can be naturally concatenated into a single, extended query sentence.\n\n[3] Ominicontrol: Minimal and universal control for diffusion transformer. ICCV, 2025\n\n[4] In-Context Edit: Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformer. arXiv:2504.20690"}, {"review_id": "AYDwty3Sv2", "reviewer": "Reviewer_ZBdY", "summary": "This paper introduces a novel Composed Person Retrieval (CPR) task, which leverages both a reference image and a textual relative caption to retrieve a target person image from a large-scale gallery. To support this task, the authors propose a scalable pipeline for automatic triplet synthesis, utilizing large language models (LLMs) and a fine-tuned generative model. Based on this pipeline, they construct SynCPR, a fully synthetic CPR dataset at million-scale. Furthermore, they present a Fine-grained Adaptive Feature Alignment (FAFA) framework tailored for the CPR task. To enable objective evaluation on real-world data, the authors also annotate existing clothes-changing person retrieval datasets with CPR-specific labels.", "questions": "1. Regarding the triplet synthesis pipeline, the authors fine-tune DiT using different LoRA strength values β to introduce style diversity, but cause the images in SynCPR exhibit varying degrees of realism depending on β. However, it seems to lack experiments to verify whether using multiple β values leads to better performance compared to a single fixed β and examine whether the inclusion of less realistic images has any negative impact on model performance. Could you clarify this point？\n\n2. Among the four settings used for comparison with existing methods, the pretraining data varies across different settings, and some baselines were not trained for the text+image (T+I) task—particularly in the first two settings. Under such circumstances, can these comparisons reliably support the claimed superiority of the FAFA approach?\n\n3. In the FAFA framework, why are the fine-grained features derived from the target image rather than the reference image?", "limitations": "1. CPR appears to require both text and image queries simultaneously, which may limit its applicability in scenarios where only one modality is available.\n\n2. ITCPR focuses mainly on clothing change, thus leading to limited evaluation of other potential variations.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths: \n1. Quality: ① The pipeline for constructing SynCPR is technically sound, with identity consistency achieved via innovative single-image pair synthesis and realistic images generated by a model fine-tuned specifically for this task. ② The experiments are comprehensive, including extensive ablation studies and comparisons with existing methods across multiple settings. ③ ITCPR provides rigorous evaluation on real clothes-changing datasets with false-negative mitigation, making the assessment more convincing.\n2. Clarity: The paper demonstrates clear writing and a coherent structure, with a fairly comprehensive description of the experimental implementation details.\n3. Significance: ① CPR complements existing retrieval tasks by combining text and image queries, demonstrating promising applicability in real-world scenarios. ② This work introduces two novel datasets: SynCPR, which eliminates the need for costly manual annotation, and ITCPR, which is derived from existing clothes-changing person retrieval datasets with additional annotations.\n4. Originality: ① First formalization of CPR, bridging IPR/TPR/CIR gaps. ② A fine-tuned generative model for images that closely resemble real-world persons generation.\n\nWeaknesses: \n1. Quality: Experiments focus on clothing changes (ITCPR) while performance on other variations (e.g., scene) is unexplored\n2. Clarity: The  icons in some figures, such as Figure 1 (b) and 3 are not distinguishable.\n3. Significance: ITCPR primarily focuses on clothing changes and lacks coverage of other types of variations.\n4. Originality: The distinction between CPR and CIR could be articulated more clearly.", "was_revised": true, "final_justification": "Most of my concerns have been addressed by the authors, I will maintain the initial score.", "rebuttal": "We sincerely thank you for your efforts and time, as well as your overall positive evaluation. In response to your crucial comments, we provide detailed clarifications below.\n\n## Responses to Weaknesses:\n\n**A1.1: Focus Mainly on Appearance Variations**\n\nWhen manually annotating the ITCPR test set, we primarily focus on appearance changes. This focus reflects many practical scenarios. For instance, when looking for a missing person, we may have a previous photograph in which the appearance often differs significantly from that on the day of disappearance. Considering that other types of variations, as you pointed out, such as changes in scenes or hairstyles and so on, also hold additional practical value, we plan to add more annotations to align with our SynCPR training set, which includes various types of variations.\n\n**A1.2: Figure Icon Issues**\n\nThank you for pointing out the issue with the icons in Figures 1(b) and 3. In the revised manuscript, we will refine both figures by adding clearer legends and supplementary annotations to improve readability and ensure that all icons are easily distinguishable.\n\n**A1.3:** This concern overlaps with our response provided in **A1.1**. Please refer to A1.1.\n\n**A1.4: Distinction Between CPR and CIR Tasks**\n\nComposed Image Retrieval (CIR) aims to retrieve a target image based on a query composed of a reference image and a relative caption that describes the difference between the two images [1]. Composed Person Retrieval (CPR) aims to retrieve a target person image. Therefore, the main distinction between CPR and CIR lies in the granularity and strictness of retrieval constraints. CIR focuses on retrieving images matching the semantic modifications described by a combination of textual and visual queries, and there are no strong constraints between the reference and target images. Typical CIR examples are provided by Figure 8 in the arXiv version of reference [1]. In contrast, CPR requires the retrieved target person to share the SAME ID as the reference individual, supplemented with additional details such as clothing attributes, thereby imposing stronger constraints. Moreover, since all images in CPR are of persons, the retrieval model must precisely distinguish subtle semantic differences among numerous visually similar candidates. Therefore, compared with CIR, the CPR task requires stronger capabilities in image relevance assessment and fine-grained semantic differentiation.\n\n[1] Zero-Shot Composed Image Retrieval with Textual Inversion. ICCV2023 or arXiv:2303.15247\n\n## Responses to Questions:\n\n**A2.1: Clarification on Using Multiple β Values**\n\nWhen the data distribution of the test domain is unknown, increasing the diversity of training data helps to enhance the model's adaptability under various distributions. To this end, via LLM-generated texts, our method first ensures diverse textual descriptions. Then, the image style diversity is controlled by adjusting the β value. As shown in supplementary Figure S11, different β values correspond to different image styles. For example, β=0 corresponds to purely generated (artificial) style images; β≈0.3 corresponds to photographic-quality images; β=1 corresponds closely to surveillance-style images. Therefore, to achieve diverse image styles and maintain a strong sense of realism for the majority of images, we adopt a strategy of setting β=1 with 50% probability during inference, while randomly sampling β values in other cases to ensure broad distribution coverage.\n\nTo verify the impact of different β strategies on the retrieval performance of FAFA, we first randomly select 10,000 triplets (5,000 quadruples) from the SynCPR dataset as a benchmark training set. Then, we respectively generate different datasets using fixed β=1, β=0, and fully random β. To ensure a fair comparison, the images that do not meet the filtering criteria are regenerated. The performance comparison is as follows:\n\n| Method | Train Data | Rank-1 | Rank-5 | Rank-10 | mAP   |\n| - | - | - | - | - | - |\n| FAFA   | β=0 | 33.70  | 56.00  | 62.81   | 43.56 |\n| FAFA   | β=1| 40.69  | 61.53  | 69.57   | 51.35 |\n| FAFA   | Random β | 40.51  | 61.58  | 69.26   | 51.27 |\n| FAFA   | Ours (mixed β) | 41.51  | 62.40  | 69.71   | 52.03 |\n\nThe results clearly show that predominantly using unrealistic images (β=0) seriously harms retrieval performance. Although a fully random β can provide broad distribution coverage, a high proportion of less realistic images slightly reduces retrieval effectiveness. Conversely, relying exclusively on highly realistic images (β=1) narrows the data distribution, causing a slight performance drop due to varying image styles from multiple sources within ITCPR. Finally, the strategy of combining realistic images with broad distributional coverage achieves the best performance. We will add the above analysis to the revised manuscript.\n\n**A2.2: Reliability of FAFA Superiority Claims in Table 1**\n\nIn Table 1, we aim to compare all promising solutions for the CPR task. To objectively evaluate the performance of each method on the CPR task, the selected training datasets for these methods can be either the ones recommended in their original works or the widely recognized datasets within their respective fields. We observe that except for the first two experimental settings (which are not specifically trained for text+image retrieval) failing to reliably demonstrate FAFA's superiority, the comprehensive results under all other settings effectively validate the superior performance of FAFA.\n\nIt should be noted that the first two settings are actually essential for demonstrating the validity of the ITCPR dataset construction and related experimental protocols. In other words, using only text, only images, or simply combining the two leads to poor performance. Only by effectively extracting complementary information from combined queries, as demonstrated by FAFA, can higher retrieval accuracy be achieved.\n\n**A2.3: Fine-grained Features Derived from Target Image**\nThe reasons for extracting fine-grained features solely from the target image are as follows:\n\n1. **Retrieval efficiency**: If fine-grained features are used on both query and target sides, matching will require solving a (k × k) bipartite graph, significantly increasing computational complexity and complicating loss design. Additionally, this approach violates our \"dynamic alignment\" principle, which emphasizes \"selection on one side and aggregation on the other.\"\n\n2. **Clear role differentiation aligned with CPR characteristics**:\n\n   * Query side: Combines a reference image and relative textual description, inherently requiring information integration to predict \"how the appearance should change.\"\n   * Target side: Must identify the correct target from numerous candidates, inherently requiring preservation of local discriminative features for fine-grained differentiation.\n\n3. **Avoiding multimodal noise interference**: On the query side, the image and textual description are integrated through the Q-Former, resulting in a semantically-completed holistic representation. The Masked Feature Reasoning (MFR) further reinforces this integration. If this unified representation is decomposed back into patch-level fine-grained vectors, textual noise and semantic confusion will be introduced at the patch level, negatively affecting retrieval performance.\n\n## Responses to Limitations:\n\n**A3.1: Modality Deficiency Experiments**\n\nThank you for providing such an insightful suggestion. Our FAFA can handle the situation of missing modalities by making minor adjustments to the training strategy. Specifically, while keeping all other settings unchanged, we retrain FAFA on SynCPR data with several modality-deficient scenarios: 70% complete inputs, 15% missing textual inputs (blank text), and 15% missing images (purely black images). During inference, the same modality-deficiency settings are applied. The resulting test performances are as follows:\n\n| Method  | Test Data  | Rank-1 | Rank-5 | Rank-10 | mAP   |\n| - | - | - | - | - | - |\n| FAFA    | Complete   | 45.87  | 65.53  | 72.52   | 54.90 |\n| SOLIDER | Image-only | 8.45   | 18.48  | 23.89   | 13.74 |\n| FIRe2   | Image-only | 10.76  | 22.84  | 29.29   | 17.00 |\n| FAFA    | Image-only | 12.08  | 24.43  | 33.65   | 19.38 |\n| RDE     | Text-only  | 26.43  | 47.41  | 56.45   | 36.43 |\n| RaSa    | Text-only  | 28.02  | 49.23  | 57.77   | 38.04 |\n| FAFA    | Text-only  | 29.61  | 51.59  | 60.22   | 39.84 |\n\nComparing these results to those reported in Table 1, we can see that the modified FAFA slightly reduces performance under combined inputs but remains highly effective. Moreover, FAFA performance under modality-deficient conditions surpasses many dedicated image-based person retrieval (IPR) [2,3] and text-based person retrieval (TPR) methods [4,5], which are specifically trained on their respective benchmark datasets.\n\n[2] Beyond appearance: a semantic controllable self-supervised learning framework for human-centric visual tasks. CVPR, 2023\n\n[3] Exploring fine-grained representation and recomposition for cloth-changing person re-identification. TIFS, 2024\n\n[4] Noisy-correspondence learning for text-to-image person re-identification. CVPR, 2024\n\n[5] RaSa: relation and sensitivity aware representation learning for text-based person search. IJCAI, 2023\n\n**A3.2:** Please see the response to A1.1 above."}, {"review_id": "nZPusXqo36", "reviewer": "Reviewer_AeXC", "summary": "This paper explores Composed Person Retrieval (CPR), a new task that utilizes a compositional query, comprising a reference image and a textual description of modifications, to retrieve (re-identify) a target person. \n\nThe paper presents two main contributions: the construction of the SynCPR dataset and the Fine-grained Adaptive Feature Alignment (FAFA) framework. \nTo generate this high-quality synthetic dataset, a novel data synthesis pipeline is proposed, which uses a Large Language Model (Qwen2.5-70B) to generate textual quadruples, a fine-tuned generative model (Flux.1) to create identity-consistent images, and a Multimodal Large Language Model (Qwen2.5-VL) to filter for high-quality data. \nBased on this dataset, the FAFA framework is designed to improve fine-grained retrieval accuracy using three key components: Fine-grained Dynamic Alignment (FDA), Feature Diversity (FD) supervision, and bidirectional Masked Feature Reasoning (MFR). \n\nExperimental results on a newly annotated benchmark, ITCPR, confirm the effectiveness of both the SynCPR dataset and the FAFA framework when compared to existing methods.", "questions": "Based on Weaknesses that I mentioned above, I'd like to suggest:\n\n1. Please justify using more fine-grained similarity (Eq.(3)) for evaluation. Compared to using the average pooled target image feature $\\bar{f_t}$ and other existing methods, how much more computational cost does ${f^i_t}^{N_t}_{i=1}$ require, and what is the performance difference?\n\n2. The justification for the practical usefulness of the proposed CPR task should be strengthened. The paper would be more compelling with a discussion of more diverse and concrete application scenarios to better motivate the task's significance.\n\n3. Please provide more comprehensive intuitions and additional analysis behind Feature Diversity (FD) and Masked Feature Reasoning (MFR).", "limitations": "I didn't see specific sections or sentences related to this and I hope this part will be further revised.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Overall, I think the paper presented sufficient contributions, but I have some conerns.\n\n**Strengths**\n1. The synthetic dataset generation pipeline is well-designed and its effectiveness in CPR tasks has been experimentally verified in Table 1. Specifically, the process of generating consistent identity image pairs for a fine-grained person retrieval task is novel and effective. Also, the open-sourcing of related codes and datasets is appreciated.\n\n2. The ITCPR benchmark for the CPR task is also valuable, as its construction involved substantial manual annotation effort and noise-handling processes.\n\n3. The training pipeline and intuition of the FAFA framework seem reasonable, and it demonstrated strong performance in the experiments.\n\n**Weaknesses**\n\n1. As stated in L219-L222, the proposed framework seems to use multiple feature vectors to represent database images. More specifically, it uses a feature similarity defined in Eq(3) during inference. However, to my knowledge, most existing CIR approaches utilize a single feature vector during the evaluation stage. This implies that the proposed method requires more storage space and computational cost during the search stage. Please justify this trade-off.\n\n2. The usefulness of the proposed CPR task is questionable. General composed image retrieval has clear usage examples, such as product searches that reflect a user's intention on an e-commerce platform. However, what about composed person retrieval? Given that a primary application of person retrieval is in surveillance systems, it is questionable how useful CPR would be. I don't think this issue renders the paper's academic contributions meaningless, but its justification is a significant issue.\n\n3. Based on Table 2, the effectiveness of Feature Diversity (FD) and Masked Feature Reasoning (MFR) seems marginal compared to that of FDA. It would be better to have additional intuition and analysis on these auxiliary losses. The current manuscript primarily discusses the numerical results. The paper would be strengthened by further clarifying the behavior and intuition of these losses through additional qualitative analysis or comparison.", "was_revised": true, "final_justification": "I appreciate the authors' additional experimental results and discussions. All of the major concerns I raised have been addressed during the rebuttal period. Taking this into account, I'd like to raise my score to 5.", "rebuttal": "Thank you very much for your valuable feedback and positive assessment. We reply to each of your concerns below.\n\n## Responses to Weaknesses/Questions:\n\n**A1.1: Efficiency and Computational Cost of Using Multiple Feature Vectors**\n\nDuring inference, existing Composed Image Retrieval (CIR) methods vary significantly in terms of the number of target-image tokens they use. For instance, among methods based on BLIP2 (CaLa and SPRC), CaLa only uses the first token as a global image feature, while SPRC uses all 32 tokens and selects the one with the highest similarity score as the final similarity. Regardless, the primary computational cost arises from feature extraction rather than feature storage or retrieval operations.\n\nIn terms of storage, CaLa stores a single 768-dimensional vector per image, while both SPRC and our FAFA store 32 vectors (each with 256 dimensions). For a database containing 10,000 images stored in half-precision format, CaLa requires approximately 15.36 MB, while SPRC and FAFA require about 163.84 MB. Such an amount of storage is relatively easy to manage.\n\nWe evaluate the inference efficiency on the entire ITCPR dataset using a single H800 GPU (the average of 10 runs, with the time unit being seconds):\n\n| Method | 2202 Query Feature Extraction | 20510 Candidate Feature Extraction | Retrieval |\n| - | - | - | - |\n| CaLa | 7.9549s | 48.7663s | 1.6892s |\n| SPRC  | 9.5462s  | 56.1453s  | 1.0406s |\n| FAFA (k=1)  | 7.1764s | 56.0928s | 1.0291s |\n| FAFA (k=6)  | 7.1778s | 56.0124s | 1.0337s |\n| FAFA (k=16) | 7.1598s | 56.0854s | 1.0445s |\n\nThese results show that feature extraction dominates computational cost. The retrieval step itself (involving simple matrix operations) consumes minimal time. Specifically, our FAFA achieves the fastest query feature extraction as it involves only a Q-Former and a visual encoder, unlike SPRC and CaLa, which include additional text encoders. Although CaLa can extract candidate features more quickly, its retrieval speed is the slowest. Importantly, varying the parameter `k` in FAFA (the number of tokens used for similarity computation) has a negligible impact on retrieval time. Therefore, selecting `k` based on retrieval accuracy is optimal; the original Figure 5(b) reports retrieval performance for `k` values from 1 to 16, with `k = 6` achieving the best performance and thus chosen as our final setting.\n\n**A1.2: Practical Justification for the CPR Task**\n\nThank you for raising this practical question. The Composed Person Retrieval (CPR) task has significant application value in multiple aspects:\n\n* **Missing person search**: Families usually provide a past photograph and verbal descriptions of the current appearance of the person involved. A CPR system integrating both image and textual descriptions can efficiently give the most relevant results from surveillance or social media images, etc. This situation happens frequently, especially with children and the elderly.\n* **Media editing and content management**: Video editors can efficiently retrieve specific clips using actor photographs combined with descriptions of desired scenes or states.\n* **Personalized fashion recommendation**: Users on e-commerce platforms can upload personal photos along with textual style descriptions, thus precisely retrieving matching outfits and enhancing shopping experiences.\n\nTherefore, CPR has broad applicability. We will incorporate this discussion in the revised manuscript.\n\n**A1.3: Intuitions and Analysis of Feature Diversity (FD) and Masked Feature Reasoning (MFR)**\n\n**Feature Diversity (FD)**\n\n*Intuition:*\nIn the CPR task, fine-grained target features precisely capture subtle appearance differences associated with the compositional query. Without FD supervision, extracted features may become redundant, limiting the ability to distinguish delicate differences. FD supervision encourages the extracted features to be sufficiently distinctive and complementary, thus fully representing the rich details within person images.\n\n*Qualitative Analysis:*\nFD loss sets a maximum cosine similarity threshold `m`, pushing features apart in the semantic space. This helps form diverse representations and enhances sensitivity to subtle variations. Specifically, the model trained with FD (No.5 in Table 2) reduces the intra-group maximum cosine similarity of target features from 0.824 (No.4 in Table 2) to 0.713, indicating that it contains richer and more diverse information and is suitable for Fine-grained Dynamic Alignment (FDA).\n\n*Theoretical Analysis:*\nFrom the perspective of representation learning, FD acts similarly to a regularization mechanism, maximizing the semantic coverage and discriminability of the target feature space. From an information-theoretic viewpoint, FD reduces redundancy and uncertainty and increases entropy, thus enhancing representational efficiency and generalization, especially when the test distribution is unknown.\n\n**Masked Feature Reasoning (MFR)**\n\n*Intuition:*\nIn the CPR task, the combination of the reference image and textual description inherently involves complementary information, as the text describes visual changes. MFR randomly masks parts of the combined representation to force the model to explicitly capture this complementarity, thus improving its capability to recover complete representations from partial information.\n\n*Qualitative Analysis:*\nMFR enhances the extraction of complementary information between reference images and text, thus improving alignment with target features. An intuitive demonstration method is to visualize the attention distributions when extracting query features and observe if textual emphasis and image regions become more complementary after adding MFR supervision. We will include this visualization in the revised manuscript (images cannot be inserted in the current reply). Additionally, the average cosine similarity between query features and FDA-derived target features increases from 0.481 (No.3 in Table 2) to 0.504 (No.5 in Table 2), indirectly confirming MFR's effectiveness in promoting complementary information extraction.\n\n*Theoretical Analysis:*\nTheoretically, MFR is similar to masked autoencoder mechanisms, improving the model's understanding of input features through a self-supervised masking constraint. Randomly masking feature dimensions can act as a self-supervised objective, promoting effective modeling of conditional probability distributions between different modalities. This implicit reconstruction objective increases the density and continuity of the cross-modal feature space and theoretically ensures that more robust and generalizable representations can be obtained.\n\n## Response to Limitations:\n\n**A2:**\nThanks for your forward-looking comments. A more detailed discussion regarding the limitations will be expanded in the revised version. Firstly, following mainstream person retrieval approaches, FAFA currently only supports taking one image and one textual description as input. However, a feasible approach for introducing multiple reference images or textual descriptions to further improve practicality is discussed in our response to Reviewer iMvu (A2.3). Additionally, the application of the CPR model under resource-constrained conditions also remains to be further addressed.\n\n## Response to Ethical Concerns:\n\n**A3.1: Data Quality and Representativeness**\n\nOur SynCPR dataset is entirely generated through an automated pipeline, involving three stages: the generation of diverse textual quadruples, the synthesis of identity-consistent images, and rigorous multimodal quality filtering. Specifically, approximately 140,000 diverse textual quadruples are generated by Qwen2.5-70B; subsequently, identity-consistent image pairs are synthesized by the Flux.1 model fine-tuned via LoRA, with additional style perturbations to enhance diversity. Finally, a strict multi-criteria filtering approach retains 1.15 million high-quality triplets. These triplets cover extensive variations in clothing, age, scene, and so on, ensuring high diversity and representativeness (Supplementary Figure S10 provides an intuitive illustration of this point). Therefore, there are no risks arising from biases or distortions.\n\n**A3.2: Human Rights and Surveillance Concerns**\n\nIt should be noted that: (1) Our SynCPR dataset is completely synthetic; all person images in it depict entirely fictional individuals, without involving any real identities or non-public materials. (2) Our ITCPR test set is constructed from publicly available datasets (Celeb-reID, PRCC, LAST), explicitly permitting secondary editing and academic usage. Therefore, there are no risks in terms of ethics or human rights."}, {"review_id": "cJYXVbAUv8", "reviewer": "Reviewer_qiB6", "summary": "This paper focuses on the topic of person retrieval. The author proposes **a new task** different from pure image retrieval or text retrieval, named composed person retrieval. In simple terms, this task uses an image and a text sentence describing changed clothing content as a combined query to search for images in the gallery, which is essentially clothing-changing REID. As a contribution to this new task, the author constructs **a pipeline for large-scale training data automatic synthesis**, thereby obtaining the SynCPR training dataset. The author also proposes **a new training framework** conducive to fine-grained alignment, named FIFA. In addition, the author constructs **a manually annotated test set** named ITCPR to more comprehensively and fairly evaluate the model's performance. The overall workload of this paper is relatively substantial.", "questions": "Here are some questions that interest me, and I would be grateful if the authors could provide answers.\n\n1. How is the identity consistency ensured in your data generation process? Limited by the capabilities of existing text-to-image models, can the method shown in Figure 3 truly ensure ID consistency? Is there any quantitative experiment to prove this?\n\n2. You only used the training set of CUHK-PEDES to fine-tune FLUX, but there are other datasets in the text person reid field, such as ICFG-PEDES and UFine6926. Should you attempt to fine-tune these datasets as well, perhaps to better enhance the real-world generalization ability of your generation model?\n\n3. The experiments in Table 1 seem unconvincing in demonstrating the effectiveness of your FAFA. You only fine-tuned the CIR method using your SynCPR dataset, while other methods are basically cross-domain experiments, making this comparison unfair. Additionally, the CIR method lacks designs for person-relevant aspects such as identity matching, so it is reasonable that your method outperforms CIR. Considering that TPR methods essentially support composed retrieval as well, you should focus on comparing with TPR methods by fine-tuning these TPR methods on your SynCPR dataset first, and then conduct a fair comparison.\n\n4. Conducting cross-domain and cross-dataset experiments can better demonstrate the effectiveness and generalizability of your method, rather than only evaluating its performance on the test set you proposed.\n\nOverall, this paper has made substantial contributions, but there are still some shortcomings. If you can address the questions raised above, I am willing to raise my score.", "limitations": "The authors have barely discussed the limitations, especially the negative social impacts of their work. The authors should seriously and thoroughly discuss the social impacts of this technology in terms of privacy, and propose some control measures to prevent the improper use of this technology.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. This paper proposes a new task named Composed Person Retrieval. The task uses both images and text as queries to search for images in the gallery. Although this task has been proposed in the general domain (composed image retrieval), such in-depth task definition and exploration of solutions are the first in the person retrieval field.\n2. This paper makes significant overall contributions, proposing a new task, a new training data synthesis strategy, a new training framework, and a new real-world test set.  \n3. The paper is well-written, fluent and easy to understand, with clear explanations of details, making it easy for readers to follow.\n\nWeaknesses:\n1. The experiments are somewhat insufficient. The authors mainly conducted two types of experiments: one is the performance comparison with previous methods, and the other is the ablation experiments to verify the effectiveness of each proposed component. In fact, I believe the focus of the experiments should lie in verifying the rationality and uniqueness of this new task. The authors should have a separate section for comprehensive analysis and experimental verification of this new task. For instance, what are the essential differences between this task and existing person retrieval tasks or traditional composed image retrieval? How much improvement can image-text combination queries bring? Etc. Conducting some qualitative and quantitative experiments to verify the practical application rationality of this task is, in my view, the most core and important part.\n\n2. The research object of this paper is persons, and it aims to utilize their biological characteristics for large-scale searches, which inevitably raises significant privacy concerns and negative social impacts. However, instead of discussing these issues or proposing reasonable control strategies against technical abuse, the authors arbitrarily claim that this research \"does not pose any obvious negative societal impacts.\"\n\n3. This paper seems to lack comparisons with some related works. As far as I know, in the field of person re-identification, there have been several studies on image-text combined retrieval, but the authors do not discuss or compare these methods. For example, relevant papers such as InstructReID and UNIReID.", "was_revised": true, "final_justification": "The contributions of this paper are sufficient, and the authors' detailed responses have addressed most of my questions. It is hoped that the authors will include the discussions in the final version.", "rebuttal": "Thank you very much for your valuable and insightful feedback. We carefully address each of your concerns as follows.\n\n## Responses to Weaknesses:\n\n**A1.1: Insufficient Experimental Validation**\n\nConsidering the space limitations, we arrange more experimental details and supplementary results in the Appendix.\nIt is worth pointing out that Table 1 is not just a performance comparison, but a comprehensive summary that addresses your multiple key concerns.\nFirstly, regarding the rationality and uniqueness of the new Composed Person Retrieval (CPR) task, the Introduction highlights its practical significance: in real-world scenarios, visual and textual information are often both accessible. For example, when elderly individuals or children go missing, which occurs frequently, we usually have previous photos as well as verbal descriptions of their current appearance. Therefore, CPR is specifically designed to leverage such complementary multimodal information for person retrieval in realistic search scenarios. Please refer to the response for Reviewer AeXC (A1.2) for more potential application examples.\n\nSecondly, regarding empirical validation, the first three experimental settings in Table 1 demonstrate that relying solely on either visual or textual modalities (as is common in existing person retrieval methods) is ineffective for CPR. In contrast, a simple combination of image and text (the 4th setting in Table 1) substantially improves retrieval performance, validating both our CPR task formulation and the relevance of the ITCPR annotations. Further improvements through our proposed FAFA framework and training on SynCPR show the importance of effectively extracting complementary multimodal information. These results also indicate that the proposed modules are indispensable.\n\nWe will provide more detailed analysis and interpretation in the revised manuscript. Additionally, we have added new experiments to strengthen the sufficiency. Please refer to the responses for Reviewer iMvu (A1.1 and A2.2), Reviewer ZBdY (A3.1), and Reviewer AeXC (A1.1).\n\n**A1.2: Privacy Concerns and Negative Social Impacts**\n\nThank you for raising these ethical concerns. It should be noted that: (1) Our SynCPR dataset is completely synthetic, with all person images depicting entirely fictional individuals, without involving any real identities or non-public materials. (2) Our ITCPR test set is constructed from publicly available datasets (Celeb-reID, PRCC, LAST), explicitly permitting secondary editing and academic usage. Therefore, there are no risks in terms of privacy issues or negative social impacts.\nIn the revised version, we will modify and clarify the descriptions that may lead to potential risks, to reflect the social responsibility and core value of \"Tech for Good\".\n\n**A1.3: Insufficient Comparisons with Related Works**\n\nThank you for suggesting relevant works, and we will add discussions of these methods in our revised Related Work section. However, it should be noted that these methods are not quite suitable for the CPR task we propose. For instance, the InstructReID model typically uses fixed textual instructions for most individuals. When it receives flexible textual instructions combined with images, the textual content tends to duplicate rather than complement the image information. Essentially, this belongs to modality fusion rather than complementary multimodal input. Consequently, InstructReID is not suitable for CPR scenarios that require complementary rather than redundant information.\n\nMoreover, the UNIReID model is limited to forensic scenarios involving sketch-plus-text retrieval and has not publicly released its trained checkpoints, limiting direct comparisons. We thus focus on experimentally comparing InstructReID's inference performance on our CPR task:\n\n| Method | Test Data | Rank-1 | Rank-5 | Rank-10 | mAP  |\n| - | - | - | - | - | - |\n| InstructReID | Text-Only   | 27.02  | 47.18  | 56.90  | 36.79 |\n| InstructReID | Combination | 26.29  | 46.50  | 56.18  | 36.21 |\n| FAFA (Ours)  | Combination | 46.54  | 66.21  | 73.12  | 55.60 |\n\nClearly, the InstructReID model fails to effectively utilize complementary multimodal information for CPR. The performance of its combined input is even lower than that of single-modal textual input, highlighting FAFA's superiority for this specific task.\n\n## Responses to Questions:\n\n**A2.1: Identity Consistency in Data Generation (Quantitative Validation)**\n\nAs described in Section 3.1, our data synthesis pipeline ensures identity consistency by generating two related sub-images simultaneously within a single image. Qualitative evidence is provided by Figure 3 in the manuscript and Figure S10 in the Appendix. Moreover, we agree that quantitative verification is necessary, and thus we conduct an additional experiment. Specifically, we randomly select 10 000 triplets (5 000 quadruples) from SynCPR and compare two generation strategies—individual image generation and simultaneous paired generation—using identical prompts and the same fine-tuned Flux.1 model. To ensure a fair comparison, generated images that do not meet the multimodal filtering criteria (excluding identity consistency) are regenerated.\n\nWe utilize the FIRe2 [1] model (pre-trained on LTCC [2]) to compute an **Identity Recall** metric. For each of the 10 000 images (5 000 identities), we retrieve the top-10 matches from the remaining images; if the same identity appears in these matches, it is regarded as a successful recall. Identity Recall is the percentage of successful recalls. Retrieval performance (Rank-1, Rank-5, Rank-10, mAP) using FAFA trained on these datasets is also reported:\n\n| Method | Training Data | Rank-1 | Rank-5 | Rank-10 | mAP | Identity Recall (%) |\n| - | -| - | - | - | - | - |\n| FAFA  | Individual generation | 37.51  | 57.81  | 67.30 | 47.11 | 2.17 |\n| FAFA | Simultaneous generation | 41.51  | 62.40  | 69.71 | 52.03 | 72.35  |\n\nThese results clearly demonstrate the effectiveness of our simultaneous-generation approach in ensuring identity consistency. This quantitative analysis will be included in the revised manuscript.\n\n[1] Exploring fine-grained representation and recomposition for cloth-changing person re-identification. TIFS, 2024\n\n[2] Long-term cloth-changing person re-identification. ACCV, 2020\n\n**A2.2: Choice of Dataset for Fine-tuning**\n\nRegarding the concern about whether all datasets should be used: our goal is to propose a feasible solution for the new CPR task, which serves as an important supplement to existing person-retrieval technologies such as text-based person retrieval (TPR), image-based person retrieval (IPR), and cloth-changing Re-ID. Therefore, UFine6926, which primarily collects images from online videos, is excluded. Among the three classic TPR datasets (CUHK-PEDES, ICFG-PEDES, RSTPReid), only CUHK-PEDES gathers images from five surveillance datasets, providing broader scene diversity. The other two datasets source their images from MSMT17, which mainly contains a single parking-lot scenario. To prevent overly homogeneous scene distributions from negatively affecting generation diversity, we choose CUHK-PEDES for fine-tuning. We will briefly add this reasoning to the revised manuscript.\n\n**A2.3: Fine-tuning TPR Methods**\n\nWe clarify a misunderstanding: all compared methods, including our own, are independently trained without referencing the ITCPR test-set distribution, meaning that every experiment constitutes a cross-domain test. Following your suggestion, we fine-tune two representative TPR methods (IRRA and RDE) on our SynCPR dataset for a fair comparison. Reference images and textual descriptions are combined into a global query feature by summing features from dual encoders, while target images are encoded into global features separately. We train these methods under two settings: training from scratch and fine-tuning from pre-trained checkpoints on TPR datasets. The results are as follows:\n\n| Method | Pretraining Data | Rank-1 | Rank-5 | Rank-10 | mAP   |\n| - | - | - | - | - | - |\n| IRRA   | – | 27.56  | 47.41  | 56.95   | 37.23 |\n| IRRA   | CUHK-PEDES | 35.33  | 55.00  | 63.81   | 44.86 |\n| RDE    | – | 30.43  | 52.68  | 61.26   | 40.85 |\n| RDE    | CUHK-PEDES       | 36.92  | 59.17  | 67.03   | 47.21 |\n| FAFA   | – | 46.54  | 66.21  | 73.12   | 55.60 |\n\nThese results show that pretraining on TPR datasets improves retrieval performance. Nevertheless, TPR methods remain far inferior to our FAFA and the fine-tuned Composed Image Retrieval (CIR) approaches (Table 1). CIR methods leverage attention-based interactions between reference images and relative textual descriptions, whereas TPR methods encode these independently, limiting their ability to exploit complementary multimodal information effectively.\n\n**A2.4: Cross-domain and Cross-dataset Experiments**\n\nAs explained in A2.3, all of our current experiments are conducted under cross-domain settings. To the best of our knowledge, CPR is a newly proposed task, and there are presently no alternative datasets available for broader testing. Nevertheless, in Table S3 of the Appendix we additionally compare FAFA on a CIR benchmark that also uses triplet supervision; FAFA still achieves state-of-the-art performance, further demonstrating the effectiveness and robustness of our method.  We welcome the Reviewer to provide datasets suitable for cross-dataset validation for the additional experiments.\n\n## Response to Limitations:\n\n**A3:** We will significantly expand our discussion of limitations and potential negative social impacts as described in response A1.2. Additionally, introducing multiple reference images or textual descriptions to improve practicality, as well as deploying the CPR model under resource-constrained conditions, remains to be explored."}]

Author rebuttal:

Meta-review: The paper introduces a new task, Composed Person Retrieval (CPR), which uses both a reference image and a textual description to identify a target person from a large gallery. Its core contributions include a scalable synthetic data generation pipeline, a fine-grained retrieval framework, and a manually annotated benchmark for evaluation.

Reviewers thought the paper proposed a novel and relevant retrieval task, presenting a technically sound synthetic data generation pipeline, and introducing a well-structured retrieval framework. Reviewers also appreciated the contributed dataset, and noted the comprehensiveness of the experiments and ablations. The clarity of writing and the potential impact of CPR in real-world applications were also seen as strengths by the general reviewers (I’ll discuss more about the ethics reviews below).

Initial concerns from reviewers included the scale and diversity of the test set, the domain gap between synthetic and real data, justification of FAFA components, computational cost, and practical utility of the CPR task. However, these were largely resolved during the rebuttal and discussion, which the reviewers indicated sufficiently addressed their concerns.

With this said, this paper was flagged for ethics reviews due to the human data and surveillance applications. The ethics reviewers raised valid concerns regarding fairness, bias in synthetic data generated using LLMs, and the dual-use potential of CPR for surveillance. Specifically, they pointed out the need for more transparency about demographic representation, fairness across groups, and the societal risks of surveillance misuse. In response, the authors made several commitments for the revised version including incorporating demographic diversity statistics and fairness metrics, adding discussion of bias mitigation strategies like prompt design and filtering, potentially embedding digital watermarks in datasets for traceability, and limiting dataset release to the academic community under responsible-use agreements. These are meaningful steps. It’s not clear that all of them are feasible for the camera ready submission, but the authors should perform due diligence to address the ethics reviewer concerns as best as possible in the camera ready. The paper should have an explicit and dedicated ethics/limitation section that states clearly what steps were taken to mitigate the ethics reviewers very valid concerns.

In addition to the ethics-focused revisions, the authors are encouraged to include the additional visualizations, extended application discussions, and clarifications they promised during the rebuttal (e.g., on FD/MFR intuitions and visualizations, and the updated figures) in the camera ready submission.

Overall, this is a strong and comprehensive paper that introduces a novel task with practical significance, and the authors have effectively addressed reviewer concerns. The AC recommends acceptance.
Decision: Accept (poster)