{"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/multimodality-adaptive-transformer-and-mutual","title":"Multimodality Adaptive Transformer and Mutual Learning for Unsupervised Domain Adaptation Vehicle Re-Identification","arxiv_id":null,"date":"2024-09-17","proceeding":"IEEE Transactions on Intelligent Transportation Systems 2024 9","authors":["Xin Zhang","Yunan Ling","Kaige Li","Weimin Shi","Zhong Zho"],"abstract":"Unsupervised Domain Adaptation Vehicle Re-Identification (UDA vehicle re-ID) aims to enable the model trained in the source domain dataset to adapt to the target domain data and obtain accurate re-identification results, which has received widespread attention due to its practicality in the field of intelligent transportation systems. Most current UDA vehicle re-ID research ignores the mining and utilization of attribute information. Meanwhile, the Convolutional Neural Networks-based (CNN-based) network will cause the loss of fine-grained information, reducing the expression and generalization ability of vehicle features. To alleviate such issues, we are motivated by the Transformer, which can exploit distinguishable attribute information and fuse multimodal features effectively. Therefore, this paper proposes a Multimodality Adaptive Transformer Network (MATNet) to intensify the ability to learn vehicle fine-grained features related to attributes. Moreover, the noise contained in pseudo-labels assigned by cluster algorithms interferes with the performance of the UDA vehicle re-ID method. We also design the Dual Mutual Dynamic Update Pseudo-Label generation strategy (DMDU) to improve the accuracy of pseudo-labels and alleviate error accumulation. The strategy is based on mutual learning, which can effectively utilize the congruous and particular knowledge of the two models to generate pseudo-labels. Extensive experiments on two large-scale public datasets, including VeRi-776 and VehicleID, illustrate that our method outperforms the state-of-the-art methods.","url_abs":"https://ieeexplore.ieee.org/abstract/document/10682436","url_pdf":"https://ieeexplore.ieee.org/abstract/document/10682436","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":[],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"pseudo-label","task_name":"Pseudo Label"},{"task_slug":"unsupervised-domain-adaptation","task_name":"Unsupervised Domain Adaptation"},{"task_slug":"vehicle-re-identification","task_name":"Vehicle Re-Identification"}],"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"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/unsupervised-domain-adaptation-on-vehicleid","task":"Unsupervised Domain Adaptation","dataset":"VehicleID to VeRi-776","model":"MATNet+DMDU","rank_in_archive_order":2,"of":14,"metrics":{"Rank-1":"79.13","Rank-10":"-","Rank-5":"88.97","mAP":"49.25"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-domain-adaptation-on-veri-776-to-2","task":"Unsupervised Domain Adaptation","dataset":"Veri-776 to VehicleID Large","model":"DMDU","rank_in_archive_order":3,"of":13,"metrics":{"R-1":"47.59","R-10":"-","R-5":"61.85","mAP":"53.97"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-domain-adaptation-on-veri-776-to-1","task":"Unsupervised Domain Adaptation","dataset":"Veri-776 to VehicleID Medium","model":"DMDU","rank_in_archive_order":3,"of":13,"metrics":{"R-1":"53.28","R-10":"-","R-5":"63.56","mAP":"56.73"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-domain-adaptation-on-veri-776-to","task":"Unsupervised Domain Adaptation","dataset":"Veri-776 to VehicleID Small","model":"DMDU","rank_in_archive_order":3,"of":8,"metrics":{" mAP":"61.83","R-1":"55.61","R-10":"-","R-5":"68.25"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}