{"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/instance-aware-context-focused-and-memory","title":"Instance-aware, Context-focused, and Memory-efficient Weakly Supervised Object Detection","arxiv_id":"2004.04725","date":"2020-04-09","proceeding":"CVPR 2020 6","authors":["Zhongzheng Ren","Zhiding Yu","Xiaodong Yang","Ming-Yu Liu","Yong Jae Lee","Alexander G. Schwing","Jan Kautz"],"abstract":"Weakly supervised learning has emerged as a compelling tool for object detection by reducing the need for strong supervision during training. However, major challenges remain: (1) differentiation of object instances can be ambiguous; (2) detectors tend to focus on discriminative parts rather than entire objects; (3) without ground truth, object proposals have to be redundant for high recalls, causing significant memory consumption. Addressing these challenges is difficult, as it often requires to eliminate uncertainties and trivial solutions. To target these issues we develop an instance-aware and context-focused unified framework. It employs an instance-aware self-training algorithm and a learnable Concrete DropBlock while devising a memory-efficient sequential batch back-propagation. Our proposed method achieves state-of-the-art results on COCO ($12.1\\% ~AP$, $24.8\\% ~AP_{50}$), VOC 2007 ($54.9\\% ~AP$), and VOC 2012 ($52.1\\% ~AP$), improving baselines by great margins. In addition, the proposed method is the first to benchmark ResNet based models and weakly supervised video object detection. Code, models, and more details will be made available at: https://github.com/NVlabs/wetectron.","url_abs":"https://arxiv.org/abs/2004.04725v3","url_pdf":"https://arxiv.org/pdf/2004.04725v3.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":"instance-aware-context-focused-and-memory","repo_url":"https://github.com/NVlabs/wetectron","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"instance-aware-context-focused-and-memory","repo_url":"https://github.com/ppengtang/pcl.pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"video-object-detection","task_name":"Video Object Detection"},{"task_slug":"weakly-supervised-object-detection","task_name":"Weakly Supervised Object Detection"},{"task_slug":"weakly-supervised-learning","task_name":"Weakly-supervised Learning"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"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":"dropblock","method_name":"DropBlock"},{"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/weakly-supervised-object-detection-on-coco-2","task":"Weakly Supervised Object Detection","dataset":"COCO test-dev","model":"wetectron(single-model, VGG16)","rank_in_archive_order":1,"of":4,"metrics":{"AP50":"24.8"},"uses_additional_data":false},{"leaderboard":"/sota/weakly-supervised-object-detection-on-pascal-1","task":"Weakly Supervised Object Detection","dataset":"PASCAL VOC 2007","model":"wetectron (single mode, 07+12)","rank_in_archive_order":7,"of":41,"metrics":{"MAP":"58.1"},"uses_additional_data":true},{"leaderboard":"/sota/weakly-supervised-object-detection-on-pascal-1","task":"Weakly Supervised Object Detection","dataset":"PASCAL VOC 2007","model":"wetectron(single-model)","rank_in_archive_order":10,"of":41,"metrics":{"MAP":"54.9"},"uses_additional_data":false},{"leaderboard":"/sota/weakly-supervised-object-detection-on-pascal","task":"Weakly Supervised Object Detection","dataset":"PASCAL VOC 2012 test","model":"wetectron(single-model)","rank_in_archive_order":9,"of":32,"metrics":{"MAP":"52.1"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2004.04725","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.04725"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/ppengtang/pcl.pytorch","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/NVlabs/wetectron","reach":{"status":"ok","spdx":"NOASSERTION"}}],"summary":{"ran":3,"unverified":2},"by_repo_kind":{"listed":{"samples":5,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"dd2212897360cbfa","entry":"dis_eval","repo":"ppengtang/pcl.pytorch","repo_kind":"listed","path":"lib/datasets/dis_eval.py","file_url":"https://github.com/ppengtang/pcl.pytorch/blob/HEAD/lib/datasets/dis_eval.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"dd2212897360cbfa"}},{"code_sha256_prefix":"7951428fcb43dcc0","entry":"filter_for_training","repo":"ppengtang/pcl.pytorch","repo_kind":"listed","path":"lib/datasets/roidb.py","file_url":"https://github.com/ppengtang/pcl.pytorch/blob/HEAD/lib/datasets/roidb.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"7951428fcb43dcc0"}},{"code_sha256_prefix":"1d0f686f173313ef","entry":"rank_for_training","repo":"ppengtang/pcl.pytorch","repo_kind":"listed","path":"lib/datasets/roidb.py","file_url":"https://github.com/ppengtang/pcl.pytorch/blob/HEAD/lib/datasets/roidb.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"1d0f686f173313ef"}},{"code_sha256_prefix":"c81c733f75b132c2","entry":"mil_losses","repo":"ppengtang/pcl.pytorch","repo_kind":"listed","path":"lib/modeling/pcl_heads.py","file_url":"https://github.com/ppengtang/pcl.pytorch/blob/HEAD/lib/modeling/pcl_heads.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c81c733f75b132c2"}},{"code_sha256_prefix":"0eafc51289a49033","entry":"parse_rec","repo":"ppengtang/pcl.pytorch","repo_kind":"listed","path":"lib/datasets/dis_eval.py","file_url":"https://github.com/ppengtang/pcl.pytorch/blob/HEAD/lib/datasets/dis_eval.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"0eafc51289a49033"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}