Text-based person search faces inherent limitations due to data scarcity, driven by stringent privacy constraints and the high cost of manual annotation. To mitigate this, existing methods usually rely on a Pretrain-then-Finetune paradigm, where models are first pretrained on synthetic person-caption data to establish cross-modal alignment, followed by fine-tuning on labeled real-world datasets. However, this paradigm lacks practicality in real-world deployment scenarios, where large-scale annotated target-domain data is typically inaccessible. In this work, we propose a new Pretrain-then-Adapt paradigm that eliminates reliance on extensive target-domain supervision through an offline test-time adaptation manner, enabling dynamic model adaptation using only unlabeled test data with minimal post-train time cost. To mitigate overconfidence with false positives of previous entropy-based test-time adaptation, we propose an Uncertainty-Aware Test-Time Adaptation (UATTA) framework, which introduces a bidirectional retrieval disagreement mechanism to estimate uncertainty, i.e., low uncertainty is assigned when an image-text pair ranks highly in both image-to-text and text-to-image retrieval, indicating high alignment; otherwise, high uncertainty is detected. This indicator drives offline test-time model recalibration without labels, effectively mitigating domain shift. We validate UATTA on four benchmarks, i.e., CUHK-PEDES, ICFG-PEDES, RSTPReid, and PAB, showing consistent improvements across both CLIP-based (one-stage) and XVLM-based (two-stage) frameworks. Ablation studies confirm that UATTA outperforms existing offline test-time adaptation strategies, establishing a new benchmark for label-efficient, deployable person search systems. Our code is available at https://github.com/nkuzjh/UATTA.

Recent progress in aligning images with texts has achieved remarkable results, however, existing models tend to serve general queries and often fall short when dealing with detailed query requirements. In this paper, we work towards Entity-centric Image-Text Matching (EITM), a finer-grained image-text matching task that aligns texts and images centered around specific entities. The main challenge in EITM lies in bridging the substantial semantic gap between entity-related information in texts and images, which is more pronounced than in general image-text matching problems. To address this challenge, we adopt CLIP as our foundational model and devise a Multimodal Attentive Experts (MMAE)-based contrastive learning to adapt CLIP into an expert for EITM problem. Particularly, the core of our multimodal attentive experts learning is to generate explanation texts by Large Language Models (LLMs) as bridging clues. In specific, we first employ off-the-shelf LLMs to generate explanatory text. This text, along with the original image and text, is then fed into our Multimodal Attentive Experts module to narrow the semantic gap within a unified semantic space. Upon the enriched feature representations generated by MMAE, we have further developed an effective Gated Integrative Image-text Matching (GI-ITM) strategy. GI-ITM utilizes an adaptive gating mechanism to combine features from MMAE, followed by applying image-text matching constraints to enhance the alignment precision. Our method has been extensively evaluated on three social media news benchmarks: N24News, VisualNews, and GoodNews. The experimental results demonstrate that our approach significantly outperforms competing methods. Our code is available at: https://github.com/wangyxxjtu/ETE.

Object re-identification (or object re-id) has gained significant attention in recent years, fueled by the increasing demand for advanced video analysis and safety systems. In object re-id, a query can be of different modalities, such as an image, a video, or natural language, containing or describing the object of interest. This workshop aims to bring together researchers, practitioners, and enthusiasts interested in object re-id to delve into the latest advancements, challenges, and opportunities in this dynamic field. The workshop covers a spectrum of topics related to object re-id, including but not limited to deep metric learning, multi-view data generation, video-based object re-id, cross-domain object re-id and real-world applications. The workshop provides a platform for researchers to showcase their work, exchange ideas, and foster potential collaborations. Additionally, it serves as a valuable opportunity for practitioners to stay abreast of the latest developments in object re-id technology.

Composed image retrieval (CIR) is the task of searching target images using an image-text pair as a query. Given the straightforward relation of query pair-target image, the dominant methods follow the learning paradigm of common image-text retrieval and simply model this problem as the query-target matching problem. Particularly, the common practice first encodes the multi-modal query into one feature and then aligns it with the target image. However, such a learning paradigm only explores the naive relation in the triplets. We argue that CIR triplets encompass additional associations besides the primary query-target relation, which is overlooked in existing works. In this paper, we disclose two new relations residing in the triplets by viewing the triplet as a graph node. In analogy with the graph node, we mine two associations of text-bridged image alignment and complementary text reasoning. The text-bridged image alignment considers composed image retrieval as a specialized form of image retrieval, where the query text acts as a bridge between the query image and the target one, and a hinge-based cross attention is proposed to incorporate this relation into the network learning. On the other hand, the association of complementary text reasoning regards composed image retrieval as a specific type of cross-modal retrieval, where the composite two images are used to reason the complementary text. To integrate these views effectively, a twin attention-based compositor is designed. By combining these two types of complementary associations with the explicit query pair-target image relation, we establish a comprehensive set of constraints for composed image retrieval. With the above designs, we finally developed our CaLa, a Complementary Association Learning framework for Augmenting Composed Image Retrieval. Experimental evaluations are conducted on the widely-used CIRR and FashIionIQ benchmarks with multiple backbones to validate the effectiveness of our CaLa. The results demonstrate the superiority of our method in the composed image retrieval task. Our code and models are available at https://github.com/Chiangsonw/CaLa

Learning an efficient news representation is a fundamental yet important problem for many tasks. Most existing news-relevant methods only take the textual information while abandoning the visual clues from the illustrations. We argue that the textual title and tags together with the visual illustrations form the main force of a piece of news and are more efficient to express the news content. In this paper, we develop a novel framework, namely Semantic Gated Network (SGN), to integrate the news title, tags and visual illustrations to obtain an efficient joint textual-visual feature for the news, by which we can directly measure the relevance between two pieces of news. Particularly, we first harvest the tag embeddings by the proposed self-supervised classification model. Besides, news title is fed into a sentence encoder pretrained by two semantically relevant news to learn efficient contextualized word vectors. Then the feature of the news title is extracted based on the learned vectors and we combine it with features of tags to obtain textual feature. Finally, we design a novel mechanism named semantic gate to adaptively fuse the textual feature and the image feature. Extensive experiments on benchmark dataset demonstrate the effectiveness of our approach.