{"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/a-unified-multi-scale-deep-convolutional","title":"A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection","arxiv_id":"1607.07155","date":"2016-07-25","proceeding":null,"authors":["Zhaowei Cai","Quanfu Fan","Rogerio S. Feris","Nuno Vasconcelos"],"abstract":"A unified deep neural network, denoted the multi-scale CNN (MS-CNN), is\nproposed for fast multi-scale object detection. The MS-CNN consists of a\nproposal sub-network and a detection sub-network. In the proposal sub-network,\ndetection is performed at multiple output layers, so that receptive fields\nmatch objects of different scales. These complementary scale-specific detectors\nare combined to produce a strong multi-scale object detector. The unified\nnetwork is learned end-to-end, by optimizing a multi-task loss. Feature\nupsampling by deconvolution is also explored, as an alternative to input\nupsampling, to reduce the memory and computation costs. State-of-the-art object\ndetection performance, at up to 15 fps, is reported on datasets, such as KITTI\nand Caltech, containing a substantial number of small objects.","url_abs":"http://arxiv.org/abs/1607.07155v1","url_pdf":"http://arxiv.org/pdf/1607.07155v1.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":"a-unified-multi-scale-deep-convolutional","repo_url":"https://github.com/zhaoweicai/mscnn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"face-detection","task_name":"Face Detection"},{"task_slug":"feature-upsampling","task_name":"Feature Upsampling"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"pedestrian-detection","task_name":"Pedestrian Detection"},{"task_slug":"real-time-object-detection","task_name":"Real-Time Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/face-detection-on-wider-face-hard","task":"Face Detection","dataset":"WIDER Face (Hard)","model":"MSCNN","rank_in_archive_order":28,"of":40,"metrics":{"AP":"0.809"},"uses_additional_data":false},{"leaderboard":"/sota/pedestrian-detection-on-caltech","task":"Pedestrian Detection","dataset":"Caltech","model":"MS-CNN","rank_in_archive_order":24,"of":33,"metrics":{"Reasonable Miss Rate":"9.95"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1607.07155","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}