{"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/gradnet-gradient-guided-network-for-visual","title":"GradNet: Gradient-Guided Network for Visual Object Tracking","arxiv_id":"1909.06800","date":"2019-09-15","proceeding":"ICCV 2019 10","authors":["Peixia Li","Bo-Yu Chen","Wanli Ouyang","Dong Wang","Xiaoyun Yang","Huchuan Lu"],"abstract":"The fully-convolutional siamese network based on template matching has shown great potentials in visual tracking. During testing, the template is fixed with the initial target feature and the performance totally relies on the general matching ability of the siamese network. However, this manner cannot capture the temporal variations of targets or background clutter. In this work, we propose a novel gradient-guided network to exploit the discriminative information in gradients and update the template in the siamese network through feed-forward and backward operations. Our algorithm performs feed-forward and backward operations to exploit the discriminative informaiton in gradients and capture the core attention of the target. To be specific, the algorithm can utilize the information from the gradient to update the template in the current frame. In addition, a template generalization training method is proposed to better use gradient information and avoid overfitting. To our knowledge, this work is the first attempt to exploit the information in the gradient for template update in siamese-based trackers. Extensive experiments on recent benchmarks demonstrate that our method achieves better performance than other state-of-the-art trackers.","url_abs":"https://arxiv.org/abs/1909.06800v1","url_pdf":"https://arxiv.org/pdf/1909.06800v1.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":"gradnet-gradient-guided-network-for-visual","repo_url":"https://github.com/LPXTT/GradNet-Pytorch","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"gradnet-gradient-guided-network-for-visual","repo_url":"https://github.com/LPXTT/GradNet-Tensorflow","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"object-tracking","task_name":"Object Tracking"},{"task_slug":"template-matching","task_name":"Template Matching"},{"task_slug":"visual-object-tracking","task_name":"Visual Object Tracking"},{"task_slug":"visual-tracking","task_name":"Visual Tracking"}],"methods":[{"method_slug":"siamese-network","method_name":"Siamese Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/visual-object-tracking-on-otb-2015","task":"Visual Object Tracking","dataset":"OTB-2015","model":"GradNet","rank_in_archive_order":18,"of":18,"metrics":{"Precision":"0.861"},"uses_additional_data":false},{"leaderboard":"/sota/visual-object-tracking-on-vot2017","task":"Visual Object Tracking","dataset":"VOT2017","model":"GradNet","rank_in_archive_order":6,"of":6,"metrics":{"Expected Average Overlap (EAO)":"0.247"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1909.06800","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.06800"}},"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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