{"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/full-duplex-strategy-for-video-object","title":"Full-Duplex Strategy for Video Object Segmentation","arxiv_id":"2108.03151","date":"2021-08-06","proceeding":"ICCV 2021 10","authors":["Ge-Peng Ji","Deng-Ping Fan","Keren Fu","Zhe Wu","Jianbing Shen","Ling Shao"],"abstract":"Previous video object segmentation approaches mainly focus on using simplex solutions between appearance and motion, limiting feature collaboration efficiency among and across these two cues. In this work, we study a novel and efficient full-duplex strategy network (FSNet) to address this issue, by considering a better mutual restraint scheme between motion and appearance in exploiting the cross-modal features from the fusion and decoding stage. Specifically, we introduce the relational cross-attention module (RCAM) to achieve bidirectional message propagation across embedding sub-spaces. To improve the model's robustness and update the inconsistent features from the spatial-temporal embeddings, we adopt the bidirectional purification module (BPM) after the RCAM. Extensive experiments on five popular benchmarks show that our FSNet is robust to various challenging scenarios (e.g., motion blur, occlusion) and achieves favourable performance against existing cutting-edges both in the video object segmentation and video salient object detection tasks. The project is publicly available at: https://dpfan.net/FSNet.","url_abs":"https://arxiv.org/abs/2108.03151v3","url_pdf":"https://arxiv.org/pdf/2108.03151v3.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":"full-duplex-strategy-for-video-object","repo_url":"https://github.com/GewelsJI/FSNet","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"salient-object-detection-1","task_name":"Salient Object Detection"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"unsupervised-video-object-segmentation","task_name":"Unsupervised Video Object Segmentation"},{"task_slug":"video-object-segmentation","task_name":"Video Object Segmentation"},{"task_slug":"video-polyp-segmentation","task_name":"Video Polyp Segmentation"},{"task_slug":"video-salient-object-detection","task_name":"Video Salient Object Detection"},{"task_slug":"video-semantic-segmentation","task_name":"Video Semantic Segmentation"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"vos","method_name":"VOS"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/unsupervised-video-object-segmentation-on-10","task":"Unsupervised Video Object Segmentation","dataset":"DAVIS 2016 val","model":"FSNet","rank_in_archive_order":17,"of":25,"metrics":{"F":"83.1","G":"83.3","J":"83.4"},"uses_additional_data":false},{"leaderboard":"/sota/video-polyp-segmentation-on-sun-seg-easy","task":"Video Polyp Segmentation","dataset":"SUN-SEG-Easy (Unseen)","model":"FSNet","rank_in_archive_order":9,"of":18,"metrics":{"Dice":"0.702","S measure":"0.725","Sensitivity":"0.493","mean E-measure":"0.695","mean F-measure":"0.630","weighted F-measure":"0.551"},"uses_additional_data":false},{"leaderboard":"/sota/video-polyp-segmentation-on-sun-seg-hard","task":"Video Polyp Segmentation","dataset":"SUN-SEG-Hard (Unseen)","model":"FSNet","rank_in_archive_order":9,"of":18,"metrics":{"Dice":"0.699","S-Measure":"0.724","Sensitivity":"0.491","mean E-measure":"0.694","mean F-measure":"0.611","weighted F-measure":"0.541"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2108.03151","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.03151"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/GewelsJI/FSNet","reach":null}],"summary":{"ran":7,"unverified":1},"by_repo_kind":{"official":{"samples":8,"ran":7,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"396efed75928c79c","entry":"BPM","repo":"GewelsJI/FSNet","repo_kind":"official","path":"lib/fsnet.py","file_url":"https://github.com/GewelsJI/FSNet/blob/HEAD/lib/fsnet.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"396efed75928c79c"}},{"code_sha256_prefix":"9d65fd2b9de9e302","entry":"BasicConv2d","repo":"GewelsJI/FSNet","repo_kind":"official","path":"lib/fsnet.py","file_url":"https://github.com/GewelsJI/FSNet/blob/HEAD/lib/fsnet.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"9d65fd2b9de9e302"}},{"code_sha256_prefix":"7d142ec657be7a17","entry":"Decoder","repo":"GewelsJI/FSNet","repo_kind":"official","path":"lib/fsnet.py","file_url":"https://github.com/GewelsJI/FSNet/blob/HEAD/lib/fsnet.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"7d142ec657be7a17"}},{"code_sha256_prefix":"b87d230de5341d9c","entry":"DimReduce","repo":"GewelsJI/FSNet","repo_kind":"official","path":"lib/fsnet.py","file_url":"https://github.com/GewelsJI/FSNet/blob/HEAD/lib/fsnet.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"b87d230de5341d9c"}},{"code_sha256_prefix":"dfbf76d4ebcee7c3","entry":"PyramidPooling","repo":"GewelsJI/FSNet","repo_kind":"official","path":"lib/fsnet.py","file_url":"https://github.com/GewelsJI/FSNet/blob/HEAD/lib/fsnet.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"dfbf76d4ebcee7c3"}},{"code_sha256_prefix":"a42e339cce788ad4","entry":"ResNet50","repo":"GewelsJI/FSNet","repo_kind":"official","path":"lib/fsnet.py","file_url":"https://github.com/GewelsJI/FSNet/blob/HEAD/lib/fsnet.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"a42e339cce788ad4"}},{"code_sha256_prefix":"5d8bdcbcbac6daf1","entry":"conv_upsample","repo":"GewelsJI/FSNet","repo_kind":"official","path":"lib/fsnet.py","file_url":"https://github.com/GewelsJI/FSNet/blob/HEAD/lib/fsnet.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"5d8bdcbcbac6daf1"}},{"code_sha256_prefix":"710857c7b9a46774","entry":"FSNet","repo":"GewelsJI/FSNet","repo_kind":"official","path":"lib/fsnet.py","file_url":"https://github.com/GewelsJI/FSNet/blob/HEAD/lib/fsnet.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"710857c7b9a46774"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}