{"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/multi-view-aggregation-network-for","title":"Multi-view Aggregation Network for Dichotomous Image Segmentation","arxiv_id":"2404.07445","date":"2024-04-11","proceeding":"CVPR 2024 1","authors":["Qian Yu","Xiaoqi Zhao","Youwei Pang","Lihe Zhang","Huchuan Lu"],"abstract":"Dichotomous Image Segmentation (DIS) has recently emerged towards high-precision object segmentation from high-resolution natural images. When designing an effective DIS model, the main challenge is how to balance the semantic dispersion of high-resolution targets in the small receptive field and the loss of high-precision details in the large receptive field. Existing methods rely on tedious multiple encoder-decoder streams and stages to gradually complete the global localization and local refinement. Human visual system captures regions of interest by observing them from multiple views. Inspired by it, we model DIS as a multi-view object perception problem and provide a parsimonious multi-view aggregation network (MVANet), which unifies the feature fusion of the distant view and close-up view into a single stream with one encoder-decoder structure. With the help of the proposed multi-view complementary localization and refinement modules, our approach established long-range, profound visual interactions across multiple views, allowing the features of the detailed close-up view to focus on highly slender structures.Experiments on the popular DIS-5K dataset show that our MVANet significantly outperforms state-of-the-art methods in both accuracy and speed. The source code and datasets will be publicly available at \\href{https://github.com/qianyu-dlut/MVANet}{MVANet}.","url_abs":"https://arxiv.org/abs/2404.07445v1","url_pdf":"https://arxiv.org/pdf/2404.07445v1.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":"multi-view-aggregation-network-for","repo_url":"https://github.com/qianyu-dlut/mvanet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"dichotomous-image-segmentation","task_name":"Dichotomous Image Segmentation"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/dichotomous-image-segmentation-on-dis-te1","task":"Dichotomous Image Segmentation","dataset":"DIS-TE1","model":"MVANet","rank_in_archive_order":2,"of":22,"metrics":{"E-measure":"0.911","HCE":"104","MAE":"0.037","S-Measure":"0.879","max F-Measure":"0.873","weighted F-measure":"0.823"},"uses_additional_data":false},{"leaderboard":"/sota/dichotomous-image-segmentation-on-dis-te2","task":"Dichotomous Image Segmentation","dataset":"DIS-TE2","model":"MVANet","rank_in_archive_order":2,"of":22,"metrics":{"E-measure":"0.944","HCE":"251","MAE":"0.030","S-Measure":"0.915","max F-Measure":"0.916","weighted F-measure":"0.874"},"uses_additional_data":false},{"leaderboard":"/sota/dichotomous-image-segmentation-on-dis-te3","task":"Dichotomous Image Segmentation","dataset":"DIS-TE3","model":"MVANet","rank_in_archive_order":2,"of":22,"metrics":{"E-measure":"0.954","HCE":"525","MAE":"0.031","S-Measure":"0.920","max F-Measure":"0.929","weighted F-measure":"0.890"},"uses_additional_data":false},{"leaderboard":"/sota/dichotomous-image-segmentation-on-dis-te4","task":"Dichotomous Image Segmentation","dataset":"DIS-TE4","model":"MVANet","rank_in_archive_order":1,"of":22,"metrics":{"E-measure":"0.944","HCE":"2331","MAE":"0.041","S-Measure":"0.903","max F-Measure":"0.912","weighted F-measure":"0.857"},"uses_additional_data":false},{"leaderboard":"/sota/dichotomous-image-segmentation-on-dis-vd","task":"Dichotomous Image Segmentation","dataset":"DIS-VD","model":"MVANet","rank_in_archive_order":4,"of":24,"metrics":{"E-measure":"0.941","HCE":"893","MAE":"0.034","S-Measure":"0.905","max F-Measure":"0.904","weighted F-measure":"0.863"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2404.07445","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.07445"}},"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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