{"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/2103-15436","title":"Transformer Tracking","arxiv_id":"2103.15436","date":"2021-03-29","proceeding":"CVPR 2021 1","authors":["Xin Chen","Bin Yan","Jiawen Zhu","Dong Wang","Xiaoyun Yang","Huchuan Lu"],"abstract":"Correlation acts as a critical role in the tracking field, especially in recent popular Siamese-based trackers. The correlation operation is a simple fusion manner to consider the similarity between the template and the search region. However, the correlation operation itself is a local linear matching process, leading to lose semantic information and fall into local optimum easily, which may be the bottleneck of designing high-accuracy tracking algorithms. Is there any better feature fusion method than correlation? To address this issue, inspired by Transformer, this work presents a novel attention-based feature fusion network, which effectively combines the template and search region features solely using attention. Specifically, the proposed method includes an ego-context augment module based on self-attention and a cross-feature augment module based on cross-attention. Finally, we present a Transformer tracking (named TransT) method based on the Siamese-like feature extraction backbone, the designed attention-based fusion mechanism, and the classification and regression head. Experiments show that our TransT achieves very promising results on six challenging datasets, especially on large-scale LaSOT, TrackingNet, and GOT-10k benchmarks. Our tracker runs at approximatively 50 fps on GPU. Code and models are available at https://github.com/chenxin-dlut/TransT.","url_abs":"https://arxiv.org/abs/2103.15436v1","url_pdf":"https://arxiv.org/pdf/2103.15436v1.pdf","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":"2103-15436","repo_url":"https://github.com/chenxin-dlut/TransT","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":null,"task_name":"GPU"},{"task_slug":"object-tracking","task_name":"Object Tracking"},{"task_slug":"video-object-tracking","task_name":"Video Object Tracking"},{"task_slug":"visual-object-tracking","task_name":"Visual Object Tracking"},{"task_slug":"visual-tracking","task_name":"Visual Tracking"}],"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/object-tracking-on-coesot","task":"Object Tracking","dataset":"COESOT","model":"TransT","rank_in_archive_order":6,"of":12,"metrics":{"Precision Rate":"67.9","Success Rate":"60.5"},"uses_additional_data":false},{"leaderboard":"/sota/video-object-tracking-on-nv-vot211","task":"Video Object Tracking","dataset":"NT-VOT211","model":"TransT","rank_in_archive_order":13,"of":43,"metrics":{"AUC":"36.79","Precision":"51.97"},"uses_additional_data":false},{"leaderboard":"/sota/visual-object-tracking-on-avist","task":"Visual Object Tracking","dataset":"AVisT","model":"TransT","rank_in_archive_order":7,"of":7,"metrics":{"Success Rate":"49.03"},"uses_additional_data":false},{"leaderboard":"/sota/visual-object-tracking-on-didi","task":"Visual Object Tracking","dataset":"DiDi","model":"TransT","rank_in_archive_order":11,"of":11,"metrics":{"Tracking quality":"0.465"},"uses_additional_data":false},{"leaderboard":"/sota/visual-object-tracking-on-lasot","task":"Visual Object Tracking","dataset":"LaSOT","model":"TransT","rank_in_archive_order":37,"of":46,"metrics":{"AUC":"64.9","Normalized Precision":"73.8","Precision":"69.0"},"uses_additional_data":false},{"leaderboard":"/sota/visual-tracking-on-tnl2k","task":"Visual Tracking","dataset":"TNL2K","model":"TransT","rank_in_archive_order":5,"of":6,"metrics":{"AUC":"50.7"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2103.15436","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.15436"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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