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One hand is that the pooling and upsampling operations in FCNs might cause blur object boundaries. On the other hand, using an additional depth-network to extract depth features might lead to high computation and storage cost. The reliance on depth inputs during testing also limits the practical applications of current RGB-D models. In this paper, we propose a novel collaborative learning framework where edge, depth and saliency are leveraged in a more efficient way, which solves those problems tactfully. The explicitly extracted edge information goes together with saliency to give more emphasis to the salient regions and object boundaries. Depth and saliency learning is innovatively integrated into the high-level feature learning process in a mutual-benefit manner. This strategy enables the network to be free of using extra depth networks and depth inputs to make inference. To this end, it makes our model more lightweight, faster and more versatile. Experiment results on seven benchmark datasets show its superior performance.","url_abs":"https://arxiv.org/abs/2007.11782v1","url_pdf":"https://arxiv.org/pdf/2007.11782v1.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":"accurate-rgb-d-salient-object-detection-via","repo_url":"https://github.com/OIPLab-DUT/CoNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"accurate-rgb-d-salient-object-detection-via","repo_url":"https://github.com/jiwei0921/CoNet","is_official":0,"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","task_name":"RGB Salient Object Detection"},{"task_slug":"rgb-d-salient-object-detection","task_name":"RGB-D Salient Object Detection"},{"task_slug":"saliency-detection","task_name":"Saliency Detection"},{"task_slug":"salient-object-detection-1","task_name":"Salient Object Detection"},{"task_slug":"thermal-image-segmentation","task_name":"Thermal Image Segmentation"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/rgb-d-salient-object-detection-on-nju2k","task":"RGB-D Salient Object Detection","dataset":"NJU2K","model":"CoNet","rank_in_archive_order":20,"of":27,"metrics":{"Average MAE":"0.047","S-Measure":"89.4"},"uses_additional_data":false},{"leaderboard":"/sota/thermal-image-segmentation-on-rgb-t-glass","task":"Thermal Image Segmentation","dataset":"RGB-T-Glass-Segmentation","model":"CoNet","rank_in_archive_order":21,"of":22,"metrics":{"MAE":"0.145"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2007.11782","atlas_url":"https://app.syntology.ai/?focus=2007.11782","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.11782"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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