Text-based person retrieval aims to identify a target individual from a gallery of images based on a natural language description. It presents a significant challenge due to the complexity of real-world scenes and the ambiguity of appearance-related descriptions. Existing methods primarily emphasize appearance-based cross-modal retrieval, often neglecting the contextual information embedded within the scene, which can offer valuable complementary insights for retrieval. To address this, we introduce SCENEPERSON-13W, a large-scale dataset featuring over 100,000 scenes with rich annotations covering both pedestrian appearance and environmental cues. Based on this, we propose SA-Person, a two-stage retrieval framework. In the first stage, it performs discriminative appearance grounding by aligning textual cues with pedestrian-specific regions. In the second stage, it introduces SceneRanker, a training-free, scene-aware re-ranking method leveraging multimodal large language models to jointly reason over pedestrian appearance and the global scene context. Experiments on SCENEPERSON-13W validate the effectiveness of our framework in challenging scene-level retrieval scenarios. The code and dataset will be made publicly available.
Text-based person retrieval aims to identify specific individuals within an image database using textual descriptions. Due to the high cost of annotation and privacy protection, researchers resort to synthesized data for the paradigm of pretraining and fine-tuning. However, these generated data often exhibit domain biases in both images and textual annotations, which largely compromise the scalability of the pre-trained model. Therefore, we introduce a domain-agnostic pretraining framework based on Cross-modality Adaptive Meta-Learning (CAMeL) to enhance the model generalization capability during pretraining to facilitate the subsequent downstream tasks. In particular, we develop a series of tasks that reflect the diversity and complexity of real-world scenarios, and introduce a dynamic error sample memory unit to memorize the history for errors encountered within multiple tasks. To further ensure multi-task adaptation, we also adopt an adaptive dual-speed update strategy, balancing fast adaptation to new tasks and slow weight updates for historical tasks. Albeit simple, our proposed model not only surpasses existing state-of-the-art methods on real-world benchmarks, including CUHK-PEDES, ICFG-PEDES, and RSTPReid, but also showcases robustness and scalability in handling biased synthetic images and noisy text annotations. Our code is available at https://github.com/Jahawn-Wen/CAMeL-reID.
Lifelong Person Re-identification (LReID) suffers from a key challenge in preserving old knowledge while adapting to new information. The existing solutions include rehearsal-based and rehearsal-free methods to address this challenge. Rehearsal-based approaches rely on knowledge distillation, continuously accumulating forgetting during the distillation process. Rehearsal-free methods insufficiently learn the distribution of each domain, leading to forgetfulness over time. To solve these issues, we propose a novel Distribution-aware Forgetting Compensation (DAFC) model that explores cross-domain shared representation learning and domain-specific distribution integration without using old exemplars or knowledge distillation. We propose a Text-driven Prompt Aggregation (TPA) that utilizes text features to enrich prompt elements and guide the prompt model to learn fine-grained representations for each instance. This can enhance the differentiation of identity information and establish the foundation for domain distribution awareness. Then, Distribution-based Awareness and Integration (DAI) is designed to capture each domain-specific distribution by a dedicated expert network and adaptively consolidate them into a shared region in high-dimensional space. In this manner, DAI can consolidate and enhance cross-domain shared representation learning while alleviating catastrophic forgetting. Furthermore, we develop a Knowledge Consolidation Mechanism (KCM) that comprises instance-level discrimination and cross-domain consistency alignment strategies to facilitate model adaptive learning of new knowledge from the current domain and promote knowledge consolidation learning between acquired domain-specific distributions, respectively. Experimental results show that our DAFC outperform state-of-the-art methods by at least 9.8\%/6.6\% and 6.4\%/6.2\% of average mAP/R@1 on two training orders.
Text-based Person Retrieval (TPR) as a multi-modal task, which aims to retrieve the target person from a pool of candidate images given a text description, has recently garnered considerable attention due to the progress of contrastive visual-language pre-trained model. Prior works leverage pre-trained CLIP to extract person visual and textual features and fully fine-tune the entire network, which have shown notable performance improvements compared to uni-modal pre-training models. However, full-tuning a large model is prone to overfitting and hinders the generalization ability. In this paper, we propose a novel Unified Parameter-Efficient Transfer Learning (PETL) method for Text-based Person Retrieval (UP-Person) to thoroughly transfer the multi-modal knowledge from CLIP. Specifically, UP-Person simultaneously integrates three lightweight PETL components including Prefix, LoRA and Adapter, where Prefix and LoRA are devised together to mine local information with task-specific information prompts, and Adapter is designed to adjust global feature representations. Additionally, two vanilla submodules are optimized to adapt to the unified architecture of TPR. For one thing, S-Prefix is proposed to boost attention of prefix and enhance the gradient propagation of prefix tokens, which improves the flexibility and performance of the vanilla prefix. For another thing, L-Adapter is designed in parallel with layer normalization to adjust the overall distribution, which can resolve conflicts caused by overlap and interaction among multiple submodules. Extensive experimental results demonstrate that our UP-Person achieves state-of-the-art results across various person retrieval datasets, including CUHK-PEDES, ICFG-PEDES and RSTPReid while merely fine-tuning 4.7\% parameters. Code is available at https://github.com/Liu-Yating/UP-Person.
Data plays a pivotal role in Text-Based Person Retrieval (TBPR) research. Mainstream research paradigm necessitates real-world person images with manual textual annotations for training models, posing privacy-sensitive and labor-intensive issues. Several pioneering efforts explore synthetic data for TBPR but still rely on real data, keeping the aforementioned issues and also resulting in diversity-deficient issue in synthetic datasets, thus impacting TBPR performance. Moreover, these works tend to explore synthetic data for TBPR through limited perspectives, leading to exploration-restricted issue. In this paper, we conduct an empirical study to explore the potential of synthetic data for TBPR, highlighting three key aspects. (1) We propose an inter-class image generation pipeline, in which an automatic prompt construction strategy is introduced to guide generative Artificial Intelligence (AI) models in generating various inter-class images without reliance on original data. (2) We develop an intra-class image augmentation pipeline, in which the generative AI models are applied to further edit the images for obtaining various intra-class images. (3) Building upon the proposed pipelines and an automatic text generation pipeline, we explore the effectiveness of synthetic data in diverse scenarios through extensive experiments. Additionally, we experimentally investigate various noise-robust learning strategies to mitigate the inherent noise in synthetic data. We will release the code, along with the synthetic large-scale dataset generated by our pipelines, which are expected to advance practical TBPR research.
With the rapid growth of multi-modal data from social media, short video platforms, and e-commerce, content-based retrieval has become essential for efficiently searching and utilizing heterogeneous information. Over time, retrieval techniques have evolved from Unimodal Retrieval (UR) to Cross-modal Retrieval (CR) and, more recently, to Composed Multi-modal Retrieval (CMR). CMR enables users to retrieve images or videos by integrating a reference visual input with textual modifications, enhancing search flexibility and precision. This paper provides a comprehensive review of CMR, covering its fundamental challenges, technical advancements, and categorization into supervised, zero-shot, and semi-supervised learning paradigms. We discuss key research directions, including data augmentation, model architecture, and loss optimization in supervised CMR, as well as transformation frameworks and external knowledge integration in zero-shot CMR. Additionally, we highlight the application potential of CMR in composed image retrieval, video retrieval, and person retrieval, which have significant implications for e-commerce, online search, and public security. Given its ability to refine and personalize search experiences, CMR is poised to become a pivotal technology in next-generation retrieval systems. A curated list of related works and resources is available at: https://github.com/kkzhang95/Awesome-Composed-Multi-modal-Retrieval
Person re-identification (Re-ID) is a critical task in human-centric intelligent systems, enabling consistent identification of individuals across different camera views using multi-modal query information. Recent studies have successfully integrated LVLMs with person Re-ID, yielding promising results. However, existing LVLM-based methods face several limitations. They rely on extracting textual embeddings from fixed templates, which are used either as intermediate features for image representation or for prompt tuning in domain-specific tasks. Furthermore, they are unable to adopt the VQA inference format, significantly restricting their broader applicability. In this paper, we propose a novel, versatile, one-for-all person Re-ID framework, ChatReID. Our approach introduces a Hierarchical Progressive Tuning (HPT) strategy, which ensures fine-grained identity-level retrieval by progressively refining the model's ability to distinguish pedestrian identities. Extensive experiments demonstrate that our approach outperforms SOTA methods across ten benchmarks in four different Re-ID settings, offering enhanced flexibility and user-friendliness. ChatReID provides a scalable, practical solution for real-world person Re-ID applications, enabling effective multi-modal interaction and fine-grained identity discrimination.
Video surveillance systems are crucial components for ensuring public safety and management in smart city. As a fundamental task in video surveillance, text-to-image person retrieval aims to retrieve the target person from an image gallery that best matches the given text description. Most existing text-to-image person retrieval methods are trained in a supervised manner that requires sufficient labeled data in the target domain. However, it is common in practice that only unlabeled data is available in the target domain due to the difficulty and cost of data annotation, which limits the generalization of existing methods in practical application scenarios. To address this issue, we propose a novel unsupervised domain adaptation method, termed Graph-Based Cross-Domain Knowledge Distillation (GCKD), to learn the cross-modal feature representation for text-to-image person retrieval in a cross-dataset scenario. The proposed GCKD method consists of two main components. Firstly, a graph-based multi-modal propagation module is designed to bridge the cross-domain correlation among the visual and textual samples. Secondly, a contrastive momentum knowledge distillation module is proposed to learn the cross-modal feature representation using the online knowledge distillation strategy. By jointly optimizing the two modules, the proposed method is able to achieve efficient performance for cross-dataset text-to-image person retrieval. acExtensive experiments on three publicly available text-to-image person retrieval datasets demonstrate the effectiveness of the proposed GCKD method, which consistently outperforms the state-of-the-art baselines.
Text-based person retrieval aims to identify the specific persons using textual descriptions as queries. Existing ad vanced methods typically depend on vision-language pre trained (VLP) models to facilitate effective cross-modal alignment. However, the inherent constraints of VLP mod-els, which include the global alignment biases and insuffi-cient self-feedback regulation, impede optimal retrieval per formance. In this paper, we propose MeFa, a Multi-Pathway Exploration, Feedback, and Adjustment framework, which deeply explores intrinsic feedback of intra and inter-modal to make targeted adjustment, thereby achieving more precise person-text associations. Specifically, we first design an intra modal reasoning pathway that generates hard negative sam ples for cross-modal data, leveraging feedback from these samples to refine intra-modal reasoning, thereby enhancing sensitivity to subtle discrepancies. Subsequently, we intro duce a cross-modal refinement pathway that utilizes both global information and intermodal feedback to refine local in formation, thus enhancing its global semantic representation. Finally, the discriminative clue correction pathway incorpo rates fine-grained features of secondary similarity as discrim inative clues to further mitigate retrieval failures caused by disparities in these features. Experimental results on three public benchmarks demonstrate that MeFa achieves superior person retrieval performance without necessitating additional data or complex structures.
Text-to-image person retrieval aims to retrieve images of person given textual descriptions, and most methods implicitly assume that the training image-text pairs are correctly aligned, but in practice, under-correlated and false-correlated problems arise for image-text pairs due to poor image quality and mislabeling. Meanwhile, the random masking augmentation strategy may incorrectly discard semantic content resulting in the problem of generating noisy pairings between image lexical elements and text descriptions. To solve these two problems, we propose a new noise label suppression method and alleviate the problem generated by random mask through an attention-weighted selective mask strategy. In the proposed noise label suppression method, the effect of noise labels is suppressed by preventing the model from being overconfident by considering the inverse KL scatter loss, which is combined with the weight adjustment focus loss to further improve the model's recognition ability on difficult samples. On the other hand, Attention-Weighted Selective Mask processes the raw image through the EMA version of the image encoder, retaining some of the tokens with strong semantic associations with the corresponding text descriptions in order to extract better features. Numerous experiments validate the effectiveness of our approach in terms of dealing with noisy problems. The code will be available soon at https://github.com/RunQing715/AMNS.git.
Occluded Person Re-Identification (ReID) is a metric learning task that involves matching occluded individuals based on their appearance. While many studies have tackled occlusions caused by objects, multi-person occlusions remain less explored. In this work, we identify and address a critical challenge overlooked by previous occluded ReID methods: the Multi-Person Ambiguity (MPA) arising when multiple individuals are visible in the same bounding box, making it impossible to determine the intended ReID target among the candidates. Inspired by recent work on prompting in vision, we introduce Keypoint Promptable ReID (KPR), a novel formulation of the ReID problem that explicitly complements the input bounding box with a set of semantic keypoints indicating the intended target. Since promptable re-identification is an unexplored paradigm, existing ReID datasets lack the pixel-level annotations necessary for prompting. To bridge this gap and foster further research on this topic, we introduce Occluded-PoseTrack ReID, a novel ReID dataset with keypoints labels, that features strong inter-person occlusions. Furthermore, we release custom keypoint labels for four popular ReID benchmarks. Experiments on person retrieval, but also on pose tracking, demonstrate that our method systematically surpasses previous state-of-the-art approaches on various occluded scenarios. Our code, dataset and annotations are available at https://github.com/VlSomers/keypoint_promptable_reidentification.
Text-based person search (TBPS) is a problem that gained significant interest within the research community. The task is that of retrieving one or more images of a specific individual based on a textual description. The multi-modal nature of the task requires learning representations that bridge text and image data within a shared latent space. Existing TBPS systems face two major challenges. One is defined as inter-identity noise that is due to the inherent vagueness and imprecision of text descriptions and it indicates how descriptions of visual attributes can be generally associated to different people; the other is the intra-identity variations, which are all those nuisances e.g. pose, illumination, that can alter the visual appearance of the same textual attributes for a given subject. To address these issues, this paper presents a novel TBPS architecture named MARS (Mae-Attribute-Relation-Sensitive), which enhances current state-of-the-art models by introducing two key components: a Visual Reconstruction Loss and an Attribute Loss. The former employs a Masked AutoEncoder trained to reconstruct randomly masked image patches with the aid of the textual description. In doing so the model is encouraged to learn more expressive representations and textual-visual relations in the latent space. The Attribute Loss, instead, balances the contribution of different types of attributes, defined as adjective-noun chunks of text. This loss ensures that every attribute is taken into consideration in the person retrieval process. Extensive experiments on three commonly used datasets, namely CUHK-PEDES, ICFG-PEDES, and RSTPReid, report performance improvements, with significant gains in the mean Average Precision (mAP) metric w.r.t. the current state of the art.
Text-based person retrieval (TPR) is a challenging task that involves retrieving a specific individual based on a textual description. Despite considerable efforts to bridge the gap between vision and language, the significant differences between these modalities continue to pose a challenge. Previous methods have attempted to align text and image samples in a modal-shared space, but they face uncertainties in optimization directions due to the movable features of both modalities and the failure to account for one-to-many relationships of image-text pairs in TPR datasets. To address this issue, we propose an effective bi-directional one-to-many embedding paradigm that offers a clear optimization direction for each sample, thus mitigating the optimization problem. Additionally, this embedding scheme generates multiple features for each sample without introducing trainable parameters, making it easier to align with several positive samples. Based on this paradigm, we propose a novel Bi-directional one-to-many Embedding Alignment (Beat) model to address the TPR task. Our experimental results demonstrate that the proposed Beat model achieves state-of-the-art performance on three popular TPR datasets, including CUHK-PEDES (65.61 R@1), ICFG-PEDES (58.25 R@1), and RSTPReID (48.10 R@1). Furthermore, additional experiments on MS-COCO, CUB, and Flowers datasets further demonstrate the potential of Beat to be applied to other image-text retrieval tasks.
Text-based Person Retrieval (TPR) aims to retrieve person images that match the description given a text query. The performance improvement of the TPR model relies on high-quality data for supervised training. However, it is difficult to construct a large-scale, high-quality TPR dataset due to expensive annotation and privacy protection. Recently, Large Language Models (LLMs) have approached or even surpassed human performance on many NLP tasks, creating the possibility to expand high-quality TPR datasets. This paper proposes an LLM-based Data Augmentation (LLM-DA) method for TPR. LLM-DA uses LLMs to rewrite the text in the current TPR dataset, achieving high-quality expansion of the dataset concisely and efficiently. These rewritten texts are able to increase the diversity of vocabulary and sentence structure while retaining the original key concepts and semantic information. In order to alleviate the hallucinations of LLMs, LLM-DA introduces a Text Faithfulness Filter (TFF) to filter out unfaithful rewritten text. To balance the contributions of original text and augmented text, a Balanced Sampling Strategy (BSS) is proposed to control the proportion of original text and augmented text used for training. LLM-DA is a plug-and-play method that can be easily integrated into various TPR models. Comprehensive experiments on three TPR benchmarks show that LLM-DA can improve the retrieval performance of current TPR models.
Text-based Person Re-identification (TPR) aims to retrieve specific individual images from datasets based on textual descriptions. Existing TPR methods primarily focus on recognizing explicit and positive characteristics, often overlooking the role of negative descriptions. This oversight can lead to false positives-images that meet positive criteria but should be excluded based on negative descriptions. To address these limitations, we introduce DualFocus, a unified framework that integrates plausible descriptions to enhance the interpretative accuracy of vision-language models in TPR tasks. DualFocus leverages Dual (Positive/Negative) Attribute Prompt Learning (DAPL), which incorporates Dual Image-Attribute Contrastive (DIAC) Learning and Sensitive Image-Attributes Matching (SIAM) Learning, enabling the detection of non-existent attributes and reducing false positives. To achieve a balance between coarse and fine-grained alignment of visual and textual embeddings, we propose the Dynamic Tokenwise Similarity (DTS) loss, which refines the representation of both matching and non-matching descriptions, thereby improving the matching process through detailed and adaptable similarity assessments. The comprehensive experiments on CUHK-PEDES, ICFG-PEDES, and RSTPReid, DualFocus demonstrates superior performance over state-of-the-art methods, significantly enhancing both precision and robustness in TPR.
Person attribute recognition and attribute-based retrieval are two core human-centric tasks. In the recognition task, the challenge is specifying attributes depending on a person's appearance, while the retrieval task involves searching for matching persons based on attribute queries. There is a significant relationship between recognition and retrieval tasks. In this study, we demonstrate that if there is a sufficiently robust network to solve person attribute recognition, it can be adapted to facilitate better performance for the retrieval task. Another issue that needs addressing in the retrieval task is the modality gap between attribute queries and persons' images. Therefore, in this paper, we present CLEAR, a unified network designed to address both tasks. We introduce a robust cross-transformers network to handle person attribute recognition. Additionally, leveraging a pre-trained language model, we construct pseudo-descriptions for attribute queries and introduce an effective training strategy to train only a few additional parameters for adapters, facilitating the handling of the retrieval task. Finally, the unified CLEAR model is evaluated on five benchmarks: PETA, PA100K, Market-1501, RAPv2, and UPAR-2024. Without bells and whistles, CLEAR achieves state-of-the-art performance or competitive results for both tasks, significantly outperforming other competitors in terms of person retrieval performance on the widely-used Market-1501 dataset.
Text-based person search aims to simultaneously localize and identify the target person based on query text from uncropped scene images, which can be regarded as the unified task of person detection and text-based person retrieval task. In this work, we propose a large-scale benchmark dataset named PRW-TPS-CN based on the widely used person search dataset PRW. Our dataset contains 47,102 sentences, which means there is quite more information than existing dataset. These texts precisely describe the person images from top to bottom, which in line with the natural description order. We also provide both Chinese and English descriptions in our dataset for more comprehensive evaluation. These characteristics make our dataset more applicable. To alleviate the inconsistency between person detection and text-based person retrieval, we take advantage of the rich texts in PRW-TPS-CN dataset. We propose to aggregate multiple texts as text prototypes to maintain the prominent text features of a person, which can better reflect the whole character of a person. The overall prototypes lead to generating the image attention map to eliminate the detection misalignment causing the decrease of text-based person retrieval. Thus, the inconsistency between person detection and text-based person retrieval is largely alleviated. We conduct extensive experiments on the PRW-TPS-CN dataset. The experimental results show the PRW-TPS-CN dataset's effectiveness and the state-of-the-art performance of our approach.
Person Re-Identification (Re-ID) task seeks to enhance the tracking of multiple individuals by surveillance cameras. It supports multimodal tasks, including text-based person retrieval and human matching. One of the most significant challenges faced in Re-ID is clothes-changing, where the same person may appear in different outfits. While previous methods have made notable progress in maintaining clothing data consistency and handling clothing change data, they still rely excessively on clothing information, which can limit performance due to the dynamic nature of human appearances. To mitigate this challenge, we propose the Pose-Guidance Deep Supervision (PGDS), an effective framework for learning pose guidance within the Re-ID task. It consists of three modules: a human encoder, a pose encoder, and a Pose-to-Human Projection module (PHP). Our framework guides the human encoder, i.e., the main re-identification model, with pose information from the pose encoder through multiple layers via the knowledge transfer mechanism from the PHP module, helping the human encoder learn body parts information without increasing computation resources in the inference stage. Through extensive experiments, our method surpasses the performance of current state-of-the-art methods, demonstrating its robustness and effectiveness for real-world applications. Our code is available at https://github.com/huyquoctrinh/PGDS.
Existing text-based person retrieval datasets often have relatively coarse-grained text annotations. This hinders the model to comprehend the fine-grained semantics of query texts in real scenarios. To address this problem, we contribute a new benchmark named \textbf{UFineBench} for text-based person retrieval with ultra-fine granularity. Firstly, we construct a new \textbf{dataset} named UFine6926. We collect a large number of person images and manually annotate each image with two detailed textual descriptions, averaging 80.8 words each. The average word count is three to four times that of the previous datasets. In addition of standard in-domain evaluation, we also propose a special \textbf{evaluation paradigm} more representative of real scenarios. It contains a new evaluation set with cross domains, cross textual granularity and cross textual styles, named UFine3C, and a new evaluation metric for accurately measuring retrieval ability, named mean Similarity Distribution (mSD). Moreover, we propose CFAM, a more efficient \textbf{algorithm} especially designed for text-based person retrieval with ultra fine-grained texts. It achieves fine granularity mining by adopting a shared cross-modal granularity decoder and hard negative match mechanism. With standard in-domain evaluation, CFAM establishes competitive performance across various datasets, especially on our ultra fine-grained UFine6926. Furthermore, by evaluating on UFine3C, we demonstrate that training on our UFine6926 significantly improves generalization to real scenarios compared with other coarse-grained datasets. The dataset and code will be made publicly available at \url{https://github.com/Zplusdragon/UFineBench}.
Text-to-image person re-identification (ReID) aims to retrieve images of a person based on a given textual description. The key challenge is to learn the relations between detailed information from visual and textual modalities. Existing works focus on learning a latent space to narrow the modality gap and further build local correspondences between two modalities. However, these methods assume that image-to-text and text-to-image associations are modality-agnostic, resulting in suboptimal associations. In this work, we show the discrepancy between image-to-text association and text-to-image association and propose CADA: Cross-Modal Adaptive Dual Association that finely builds bidirectional image-text detailed associations. Our approach features a decoder-based adaptive dual association module that enables full interaction between visual and textual modalities, allowing for bidirectional and adaptive cross-modal correspondence associations. Specifically, the paper proposes a bidirectional association mechanism: Association of text Tokens to image Patches (ATP) and Association of image Regions to text Attributes (ARA). We adaptively model the ATP based on the fact that aggregating cross-modal features based on mistaken associations will lead to feature distortion. For modeling the ARA, since the attributes are typically the first distinguishing cues of a person, we propose to explore the attribute-level association by predicting the masked text phrase using the related image region. Finally, we learn the dual associations between texts and images, and the experimental results demonstrate the superiority of our dual formulation. Codes will be made publicly available.