{"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/unsupervised-moving-object-detection-via","title":"Unsupervised Moving Object Detection via Contextual Information Separation","arxiv_id":"1901.03360","date":"2019-01-10","proceeding":"CVPR 2019 6","authors":["Yanchao Yang","Antonio Loquercio","Davide Scaramuzza","Stefano Soatto"],"abstract":"We propose an adversarial contextual model for detecting moving objects in\nimages. A deep neural network is trained to predict the optical flow in a\nregion using information from everywhere else but that region (context), while\nanother network attempts to make such context as uninformative as possible. The\nresult is a model where hypotheses naturally compete with no need for explicit\nregularization or hyper-parameter tuning. Although our method requires no\nsupervision whatsoever, it outperforms several methods that are pre-trained on\nlarge annotated datasets. Our model can be thought of as a generalization of\nclassical variational generative region-based segmentation, but in a way that\navoids explicit regularization or solution of partial differential equations at\nrun-time.","url_abs":"http://arxiv.org/abs/1901.03360v2","url_pdf":"http://arxiv.org/pdf/1901.03360v2.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":"unsupervised-moving-object-detection-via","repo_url":"https://github.com/antonilo/unsupervised_detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"moving-object-detection","task_name":"Moving Object Detection"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"optical-flow-estimation","task_name":"Optical Flow Estimation"},{"task_slug":"unsupervised-video-object-segmentation","task_name":"Unsupervised Video Object Segmentation"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1901.03360","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.03360"}},"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/antonilo/unsupervised_detection","reach":null}],"summary":{"ran_violates":1},"by_repo_kind":{"listed":{"samples":1,"ran":1,"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":"84a3be99da7ba2b7","entry":"compute_mae","repo":"antonilo/unsupervised_detection","repo_kind":"listed","path":"test_generator.py","file_url":"https://github.com/antonilo/unsupervised_detection/blob/HEAD/test_generator.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"84a3be99da7ba2b7"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}