{"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/evidential-deep-learning-for-open-set-action","title":"Evidential Deep Learning for Open Set Action Recognition","arxiv_id":"2107.10161","date":"2021-07-21","proceeding":"ICCV 2021 10","authors":["Wentao Bao","Qi Yu","Yu Kong"],"abstract":"In a real-world scenario, human actions are typically out of the distribution from training data, which requires a model to both recognize the known actions and reject the unknown. Different from image data, video actions are more challenging to be recognized in an open-set setting due to the uncertain temporal dynamics and static bias of human actions. In this paper, we propose a Deep Evidential Action Recognition (DEAR) method to recognize actions in an open testing set. Specifically, we formulate the action recognition problem from the evidential deep learning (EDL) perspective and propose a novel model calibration method to regularize the EDL training. Besides, to mitigate the static bias of video representation, we propose a plug-and-play module to debias the learned representation through contrastive learning. Experimental results show that our DEAR method achieves consistent performance gain on multiple mainstream action recognition models and benchmarks. Code and pre-trained models are available at {\\small{\\url{https://www.rit.edu/actionlab/dear}}}.","url_abs":"https://arxiv.org/abs/2107.10161v2","url_pdf":"https://arxiv.org/pdf/2107.10161v2.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":"evidential-deep-learning-for-open-set-action","repo_url":"https://github.com/Cogito2012/DEAR","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"evidential-deep-learning-for-open-set-action","repo_url":"https://github.com/jun-cen/psl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"open-set-action-recognition","task_name":"Open Set Action Recognition"},{"task_slug":"open-set-learning","task_name":"Open Set Learning"},{"task_slug":"out-of-distribution-detection","task_name":"Out-of-Distribution Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2107.10161","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.10161"}},"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/Cogito2012/DEAR","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/jun-cen/psl","reach":null}],"summary":{"ran":4,"ran_draft_wrong":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1},"listed":{"samples":4,"ran":4,"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":4,"samples":[{"code_sha256_prefix":"410c70b574276262","entry":"BayesianLinear","repo":"jun-cen/psl","repo_kind":"listed","path":"models/base/bnn.py","file_url":"https://github.com/jun-cen/psl/blob/HEAD/models/base/bnn.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"410c70b574276262"}},{"code_sha256_prefix":"0769d9c3ce65bd1b","entry":"BayesianPredictor","repo":"jun-cen/psl","repo_kind":"listed","path":"models/base/bnn.py","file_url":"https://github.com/jun-cen/psl/blob/HEAD/models/base/bnn.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"0769d9c3ce65bd1b"}},{"code_sha256_prefix":"0532a5136c392c2c","entry":"Gaussian","repo":"jun-cen/psl","repo_kind":"listed","path":"models/base/bnn.py","file_url":"https://github.com/jun-cen/psl/blob/HEAD/models/base/bnn.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"0532a5136c392c2c"}},{"code_sha256_prefix":"22d0e548db0cef79","entry":"ScaleMixtureGaussian","repo":"jun-cen/psl","repo_kind":"listed","path":"models/base/bnn.py","file_url":"https://github.com/jun-cen/psl/blob/HEAD/models/base/bnn.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"22d0e548db0cef79"}},{"code_sha256_prefix":"18fa2e0122a637f6","entry":"evidential_prediction","repo":"Cogito2012/DEAR","repo_kind":"official","path":"demo/demo_dear.py","file_url":"https://github.com/Cogito2012/DEAR/blob/HEAD/demo/demo_dear.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"18fa2e0122a637f6"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}