{"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/rgb-d-saliency-detection-via-cascaded-mutual","title":"RGB-D Saliency Detection via Cascaded Mutual Information Minimization","arxiv_id":"2109.07246","date":"2021-09-15","proceeding":"ICCV 2021 10","authors":["Jing Zhang","Deng-Ping Fan","Yuchao Dai","Xin Yu","Yiran Zhong","Nick Barnes","Ling Shao"],"abstract":"Existing RGB-D saliency detection models do not explicitly encourage RGB and depth to achieve effective multi-modal learning. In this paper, we introduce a novel multi-stage cascaded learning framework via mutual information minimization to \"explicitly\" model the multi-modal information between RGB image and depth data. Specifically, we first map the feature of each mode to a lower dimensional feature vector, and adopt mutual information minimization as a regularizer to reduce the redundancy between appearance features from RGB and geometric features from depth. We then perform multi-stage cascaded learning to impose the mutual information minimization constraint at every stage of the network. Extensive experiments on benchmark RGB-D saliency datasets illustrate the effectiveness of our framework. Further, to prosper the development of this field, we contribute the largest (7x larger than NJU2K) dataset, which contains 15,625 image pairs with high quality polygon-/scribble-/object-/instance-/rank-level annotations. Based on these rich labels, we additionally construct four new benchmarks with strong baselines and observe some interesting phenomena, which can motivate future model design. Source code and dataset are available at \"https://github.com/JingZhang617/cascaded_rgbd_sod\".","url_abs":"https://arxiv.org/abs/2109.07246v2","url_pdf":"https://arxiv.org/pdf/2109.07246v2.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":"rgb-d-saliency-detection-via-cascaded-mutual","repo_url":"https://github.com/jingzhang617/cascaded_rgbd_sod","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"saliency-detection","task_name":"Saliency Detection"},{"task_slug":"thermal-image-segmentation","task_name":"Thermal Image Segmentation"}],"methods":[],"datasets_introduced":[{"slug":"come15k","name":"COME15K","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/thermal-image-segmentation-on-rgb-t-glass","task":"Thermal Image Segmentation","dataset":"RGB-T-Glass-Segmentation","model":"CLNet","rank_in_archive_order":5,"of":22,"metrics":{"MAE":"0.041"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2109.07246","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.07246"}},"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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