{"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/seqtr-a-simple-yet-universal-network-for","title":"SeqTR: A Simple yet Universal Network for Visual Grounding","arxiv_id":"2203.16265","date":"2022-03-30","proceeding":null,"authors":["Chaoyang Zhu","Yiyi Zhou","Yunhang Shen","Gen Luo","Xingjia Pan","Mingbao Lin","Chao Chen","Liujuan Cao","Xiaoshuai Sun","Rongrong Ji"],"abstract":"In this paper, we propose a simple yet universal network termed SeqTR for visual grounding tasks, e.g., phrase localization, referring expression comprehension (REC) and segmentation (RES). The canonical paradigms for visual grounding often require substantial expertise in designing network architectures and loss functions, making them hard to generalize across tasks. To simplify and unify the modeling, we cast visual grounding as a point prediction problem conditioned on image and text inputs, where either the bounding box or binary mask is represented as a sequence of discrete coordinate tokens. Under this paradigm, visual grounding tasks are unified in our SeqTR network without task-specific branches or heads, e.g., the convolutional mask decoder for RES, which greatly reduces the complexity of multi-task modeling. In addition, SeqTR also shares the same optimization objective for all tasks with a simple cross-entropy loss, further reducing the complexity of deploying hand-crafted loss functions. Experiments on five benchmark datasets demonstrate that the proposed SeqTR outperforms (or is on par with) the existing state-of-the-arts, proving that a simple yet universal approach for visual grounding is indeed feasible. Source code is available at https://github.com/sean-zhuh/SeqTR.","url_abs":"https://arxiv.org/abs/2203.16265v2","url_pdf":"https://arxiv.org/pdf/2203.16265v2.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":"seqtr-a-simple-yet-universal-network-for","repo_url":"https://github.com/sean-zhuh/seqtr","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"seqtr-a-simple-yet-universal-network-for","repo_url":"https://github.com/luogen1996/simrec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"seqtr-a-simple-yet-universal-network-for","repo_url":"https://github.com/seanzhuh/seqtr","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"referring-expression","task_name":"Referring Expression"},{"task_slug":"referring-expression-comprehension","task_name":"Referring Expression Comprehension"},{"task_slug":"referring-expression-segmentation","task_name":"Referring Expression Segmentation"},{"task_slug":"visual-grounding","task_name":"Visual Grounding"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/referring-expression-segmentation-on-refcoco-8","task":"Referring Expression Segmentation","dataset":"RefCOCO testA","model":"SeqTR","rank_in_archive_order":12,"of":13,"metrics":{"Overall IoU":" 69.79"},"uses_additional_data":false},{"leaderboard":"/sota/referring-expression-segmentation-on-refcoco-9","task":"Referring Expression Segmentation","dataset":"RefCOCO testB","model":"SeqTR","rank_in_archive_order":12,"of":13,"metrics":{"Overall IoU":" 64.12"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2203.16265","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.16265"}},"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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