{"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/dynamic-context-sensitive-filtering-network","title":"Dynamic Context-Sensitive Filtering Network for Video Salient Object Detection","arxiv_id":null,"date":"2021-01-01","proceeding":"ICCV 2021 10","authors":["Miao Zhang","Jie Liu","Yifei Wang","Yongri Piao","Shunyu Yao","Wei Ji","Jingjing Li","Huchuan Lu","Zhongxuan Luo"],"abstract":"    The ability to capture inter-frame dynamics has been critical to the development of video salient object detection (VSOD). While many works have achieved great success in this field, a deeper insight into its dynamic nature should be developed. In this work, we aim to answer the following questions: How can a model adjust itself to dynamic variations as well as perceive fine differences in the real-world environment; How are the temporal dynamics well introduced into spatial information over time? To this end, we propose a dynamic context-sensitive filtering network (DCFNet) equipped with a dynamic context-sensitive filtering module (DCFM) and an effective bidirectional dynamic fusion strategy. The proposed DCFM sheds new light on dynamic filter generation by extracting location-related affinities between consecutive frames. Our bidirectional dynamic fusion strategy encourages the interaction of spatial and temporal information in a dynamic manner. Experimental results demonstrate that our proposed method can achieve state-of-the-art performance on most VSOD datasets while ensuring a real-time speed of 28 fps. The source code is publicly available at https://github.com/OIPLab-DUT/DCFNet.    ","url_abs":"http://openaccess.thecvf.com//content/ICCV2021/html/Zhang_Dynamic_Context-Sensitive_Filtering_Network_for_Video_Salient_Object_Detection_ICCV_2021_paper.html","url_pdf":"http://openaccess.thecvf.com//content/ICCV2021/papers/Zhang_Dynamic_Context-Sensitive_Filtering_Network_for_Video_Salient_Object_Detection_ICCV_2021_paper.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":"dynamic-context-sensitive-filtering-network","repo_url":"https://github.com/oiplab-dut/dcfnet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"salient-object-detection-1","task_name":"Salient Object Detection"},{"task_slug":"video-polyp-segmentation","task_name":"Video Polyp Segmentation"},{"task_slug":"video-salient-object-detection","task_name":"Video Salient Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-polyp-segmentation-on-sun-seg-easy","task":"Video Polyp Segmentation","dataset":"SUN-SEG-Easy (Unseen)","model":"DCF","rank_in_archive_order":15,"of":18,"metrics":{"Dice":"0.325","S measure":"0.523","Sensitivity":"0.340","mean E-measure":"0.514","mean F-measure":"0.312","weighted F-measure":"0.270"},"uses_additional_data":false},{"leaderboard":"/sota/video-polyp-segmentation-on-sun-seg-hard","task":"Video Polyp Segmentation","dataset":"SUN-SEG-Hard (Unseen)","model":"DCF","rank_in_archive_order":15,"of":18,"metrics":{"Dice":"0.317","S-Measure":"0.514","Sensitivity":"0.364","mean E-measure":"0.522","mean F-measure":"0.303","weighted F-measure":"0.263"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}