{"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-multipath-network-for-object-detection","title":"A MultiPath Network for Object Detection","arxiv_id":"1604.02135","date":"2016-04-07","proceeding":null,"authors":["Sergey Zagoruyko","Adam Lerer","Tsung-Yi Lin","Pedro O. Pinheiro","Sam Gross","Soumith Chintala","Piotr Dollár"],"abstract":"The recent COCO object detection dataset presents several new challenges for\nobject detection. In particular, it contains objects at a broad range of\nscales, less prototypical images, and requires more precise localization. To\naddress these challenges, we test three modifications to the standard Fast\nR-CNN object detector: (1) skip connections that give the detector access to\nfeatures at multiple network layers, (2) a foveal structure to exploit object\ncontext at multiple object resolutions, and (3) an integral loss function and\ncorresponding network adjustment that improve localization. The result of these\nmodifications is that information can flow along multiple paths in our network,\nincluding through features from multiple network layers and from multiple\nobject views. We refer to our modified classifier as a \"MultiPath\" network. We\ncouple our MultiPath network with DeepMask object proposals, which are well\nsuited for localization and small objects, and adapt our pipeline to predict\nsegmentation masks in addition to bounding boxes. The combined system improves\nresults over the baseline Fast R-CNN detector with Selective Search by 66%\noverall and by 4x on small objects. It placed second in both the COCO 2015\ndetection and segmentation challenges.","url_abs":"http://arxiv.org/abs/1604.02135v2","url_pdf":"http://arxiv.org/pdf/1604.02135v2.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-multipath-network-for-object-detection","repo_url":"https://github.com/facebookresearch/multipathnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"torch","reach":null}],"tasks":[{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"deepmask","method_name":"DeepMask"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"fast-r-cnn","method_name":"Fast R-CNN"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"roipool","method_name":"RoIPool"},{"method_slug":"selective-search","method_name":"Selective Search"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/instance-segmentation-on-coco","task":"Instance Segmentation","dataset":"COCO test-dev","model":"MultiPath Network","rank_in_archive_order":106,"of":112,"metrics":{"mask AP":"25.0%"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1604.02135","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}