{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/attribute-de-biased-vision-transformer-ad-vit","title":"Attribute De-biased Vision Transformer (AD-ViT) for Long-Term Person Re-identification","arxiv_id":null,"date":"2022-11-29","proceeding":"IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) 2022 11","authors":["Kyung Won Lee","Bhavin Jawade","Deen Mohan","Srirangaraj Setlur","and Venu Govindaraju"],"abstract":"Person re-identification (re-ID) aims to retrieve images of the same identity from a gallery of person images across cameras and viewpoints. However, most works in person re-ID assume a short-term setting characterized by invariance in appearance. In contrast, a high visual variance can be frequently seen in a long-term setting due to changes in apparel and accessories, which makes the task more challenging. Therefore, learning identity-specific features agnostic of temporally variant features is crucial for robust long-term person Re-ID. To this end, we propose an Attribute De-biased Vision Transformer (AD-ViT) to provide direct supervision to learn identity-specific features. Specifically, we produce attribute labels for person instances and utilize them to guide our model to focus on identity features through gradient reversal. Our experiments on two longterm re-ID datasets - LTCC and NKUP show that the proposed work consistently outperforms current state-of-theart methods.","url_abs":"https://ieeexplore.ieee.org/abstract/document/9959509","url_pdf":"https://ieeexplore.ieee.org/abstract/document/9959509","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"attribute-de-biased-vision-transformer-ad-vit","repo_url":"https://github.com/kyungwon213/AD_ViT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"person-identification","task_name":"Person Identification"},{"task_slug":"person-re-identification","task_name":"Person Re-Identification"},{"task_slug":"person-recognition","task_name":"Person Recognition"},{"task_slug":"person-retrieval","task_name":"Person Retrieval"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"},{"method_slug":"vision-transformer","method_name":"Vision Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/person-re-identification-on-ltcc","task":"Person Re-Identification","dataset":"LTCC","model":"AD-ViT","rank_in_archive_order":13,"of":13,"metrics":{" Rank-1":"-1"," mAP":"-1","Rank-1":"-1","mAP":"-1"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}