While document summarization with LLMs has enhanced access to textual information, concerns about the factual accuracy of these summaries persist, especially in the medical domain.Tracing source evidence from which summaries are derived enables users to assess their accuracy, thereby alleviating this concern.In this paper, we introduce TRACSUM, a novel benchmark for traceable, aspect-based summarization, in which generated summaries are paired with sentence-level citations, enabling users to trace back to the original context.First, we annotate 500 medical abstracts 1 for seven key medical aspects, yielding 3.5K summarycitations pairs.We then propose a fine-grained evaluation framework for this new task, designed to assess the completeness and consistency of generated content using four metrics.Finally, we introduce a summarization pipeline, TRACK-THEN-SUM, which serves as a baseline method for comparison.In experiments, we evaluate both this baseline and a set of LLMs on TRACSUM, and conduct a human evaluation to assess the evaluation results.The findings demonstrate that TRACSUM can serve as an effective benchmark for traceable, aspectbased summarization tasks.We also observe that explicitly performing sentence-level tracking prior to summarization enhances generation accuracy, while incorporating the full context further improves completeness.

Visible-infrared person re-identification (VI-ReID) is a challenging task because the different imaging principles of visible and infrared images bring about huge modality discrepancy. Existing methods primarily address this issue by generating intermediate images to align modality features and establish connections between the visible and infrared modalities. However, the quality of these generated images is often unstable, limiting the effectiveness of such approaches. To overcome this limitation, we propose a novel method called modality shared-specific features cooperative separation. It consists of two key modules: the saliency response module and the cooperative separation module, aimed at alleviating the modality gap. The saliency response module incorporates a location attention mechanism and local features to construct contextual connections and extract local saliency information. Then, the cooperative separation module employs a more concise dual-MLPs as generator to effectively separate shared-specific features. Additionally, we introduce a shared feature refinement mechanism in both the generator and discriminator. By coordinating the shared-specific features, our method achieves secondary separation and extracts purer modality-shared features without specific information. Extensive experiments conducted on the SYSU-MM01 and RegDB public datasets demonstrate that our proposed method performs excellently in VI-ReID.

Visible-infrared Person Re-identification (VI-ReID) aims to retrieve the images of pedestrian with the same identity from different modalities and cameras given a pedestrian image. To reduce modality discrepancy, existing methods often perform hard partitioning to mine more detail. However, these methods employ only uniform partitioning, without considering pedestrian structure, and lose a lot of pedestrian semantic information. To this end, this paper proposes a structural semantic representation reconstruction (SSRR) method to capture pedestrian semantic information by focusing on pedestrian structure. Specifically, based on the fine-grained features obtained by hard partitioning, we carry out structural reconstruction to obtain the reconstructed features containing semantic information. By adopting the direct link reconstruction structure, the reciprocal learning of fine-grained features and semantic features is ensured. Semantic features are reconstructed based on fine-grained features, and semantic information is beneficial to fine-grained features to better capture pedestrian-related details. In addition, local consistency loss is introduced to ensure the consistency of fine-grained features in the same component location, further enhancing the discriminant of the learned reconstructed representation. Extensive experiments confirm the superiority of our method on two public datasets SYSU-MM01 and RegDB.