{"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/casenet-deep-category-aware-semantic-edge","title":"CASENet: Deep Category-Aware Semantic Edge Detection","arxiv_id":"1705.09759","date":"2017-05-27","proceeding":"CVPR 2017 7","authors":["Zhiding Yu","Chen Feng","Ming-Yu Liu","Srikumar Ramalingam"],"abstract":"Boundary and edge cues are highly beneficial in improving a wide variety of\nvision tasks such as semantic segmentation, object recognition, stereo, and\nobject proposal generation. Recently, the problem of edge detection has been\nrevisited and significant progress has been made with deep learning. While\nclassical edge detection is a challenging binary problem in itself, the\ncategory-aware semantic edge detection by nature is an even more challenging\nmulti-label problem. We model the problem such that each edge pixel can be\nassociated with more than one class as they appear in contours or junctions\nbelonging to two or more semantic classes. To this end, we propose a novel\nend-to-end deep semantic edge learning architecture based on ResNet and a new\nskip-layer architecture where category-wise edge activations at the top\nconvolution layer share and are fused with the same set of bottom layer\nfeatures. We then propose a multi-label loss function to supervise the fused\nactivations. We show that our proposed architecture benefits this problem with\nbetter performance, and we outperform the current state-of-the-art semantic\nedge detection methods by a large margin on standard data sets such as SBD and\nCityscapes.","url_abs":"http://arxiv.org/abs/1705.09759v1","url_pdf":"http://arxiv.org/pdf/1705.09759v1.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":"casenet-deep-category-aware-semantic-edge","repo_url":"https://github.com/Chrisding/cityscapes-preprocess","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"casenet-deep-category-aware-semantic-edge","repo_url":"https://github.com/Chrisding/sbd-preprocess","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"casenet-deep-category-aware-semantic-edge","repo_url":"https://github.com/Chrisding/seal","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"casenet-deep-category-aware-semantic-edge","repo_url":"https://github.com/Lavender105/DFF","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"casenet-deep-category-aware-semantic-edge","repo_url":"https://github.com/anirudh-chakravarthy/CASENet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"casenet-deep-category-aware-semantic-edge","repo_url":"https://github.com/arsenal9971/shearlet_semantic_edge","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"casenet-deep-category-aware-semantic-edge","repo_url":"https://github.com/lijiaman/CASENet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"casenet-deep-category-aware-semantic-edge","repo_url":"https://github.com/milongo/CASENet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"casenet-deep-category-aware-semantic-edge","repo_url":"https://github.com/yhcool14/cityscapes-preprocess","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"casenet-deep-category-aware-semantic-edge","repo_url":"https://github.com/yhcool14/sbd-preprocess","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"casenet-deep-category-aware-semantic-edge","repo_url":"https://github.com/zhusiling/SEAL","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"edge-detection","task_name":"Edge Detection"},{"task_slug":"object-proposal-generation","task_name":"Object Proposal Generation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/edge-detection-on-cityscapes","task":"Edge Detection","dataset":"Cityscapes test","model":"CASENet","rank_in_archive_order":2,"of":2,"metrics":{"AP":"70.8%","Maximum F-measure":"71.3%"},"uses_additional_data":false},{"leaderboard":"/sota/edge-detection-on-sbd","task":"Edge Detection","dataset":"SBD","model":"CASENet","rank_in_archive_order":1,"of":2,"metrics":{"Maximum F-measure":"71.4%"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1705.09759","atlas_url":"https://app.syntology.ai/?focus=1705.09759","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1705.09759"}},"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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