{"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/context-de-confounded-emotion-recognition","title":"Context De-confounded Emotion Recognition","arxiv_id":"2303.11921","date":"2023-03-21","proceeding":"CVPR 2023 1","authors":["Dingkang Yang","Zhaoyu Chen","Yuzheng Wang","Shunli Wang","Mingcheng Li","Siao Liu","Xiao Zhao","Shuai Huang","Zhiyan Dong","Peng Zhai","Lihua Zhang"],"abstract":"Context-Aware Emotion Recognition (CAER) is a crucial and challenging task that aims to perceive the emotional states of the target person with contextual information. Recent approaches invariably focus on designing sophisticated architectures or mechanisms to extract seemingly meaningful representations from subjects and contexts. However, a long-overlooked issue is that a context bias in existing datasets leads to a significantly unbalanced distribution of emotional states among different context scenarios. Concretely, the harmful bias is a confounder that misleads existing models to learn spurious correlations based on conventional likelihood estimation, significantly limiting the models' performance. To tackle the issue, this paper provides a causality-based perspective to disentangle the models from the impact of such bias, and formulate the causalities among variables in the CAER task via a tailored causal graph. Then, we propose a Contextual Causal Intervention Module (CCIM) based on the backdoor adjustment to de-confound the confounder and exploit the true causal effect for model training. CCIM is plug-in and model-agnostic, which improves diverse state-of-the-art approaches by considerable margins. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our CCIM and the significance of causal insight.","url_abs":"https://arxiv.org/abs/2303.11921v2","url_pdf":"https://arxiv.org/pdf/2303.11921v2.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":"context-de-confounded-emotion-recognition","repo_url":"https://github.com/ydk122024/ccim","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"emotion-recognition","task_name":"Emotion Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2303.11921","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.11921"}},"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/ydk122024/ccim","reach":null}],"summary":{"ran":3,"ran_draft_wrong":1,"unverified":1},"by_repo_kind":{"official":{"samples":4,"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":1,"samples":[{"code_sha256_prefix":"97925b5a06d3f45d","entry":"CCIM","repo":"ydk122024/ccim","repo_kind":"official","path":"CCIM.py","file_url":"https://github.com/ydk122024/ccim/blob/HEAD/CCIM.py","link_basis":"first_harvest_node","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":"97925b5a06d3f45d"}},{"code_sha256_prefix":"2f79ff7e2381ff18","entry":"additive_intervention","repo":"ydk122024/ccim","repo_kind":"official","path":"CCIM.py","file_url":"https://github.com/ydk122024/ccim/blob/HEAD/CCIM.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"2f79ff7e2381ff18"}},{"code_sha256_prefix":"3dd048e9824c806b","entry":"classifier","repo":"ydk122024/ccim","repo_kind":"official","path":"CCIM.py","file_url":"https://github.com/ydk122024/ccim/blob/HEAD/CCIM.py","link_basis":"first_harvest_node","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":"3dd048e9824c806b"}},{"code_sha256_prefix":"d9dcfb510a5e2690","entry":"gelu","repo":null,"repo_kind":null,"path":null,"file_url":null,"link_basis":"identical_code_first_harvested_elsewhere","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"mcp_get_code":{"code_sha256":"d9dcfb510a5e2690"}},{"code_sha256_prefix":"e1e8244b962d9c28","entry":"dot_product_intervention","repo":"ydk122024/ccim","repo_kind":"official","path":"CCIM.py","file_url":"https://github.com/ydk122024/ccim/blob/HEAD/CCIM.py","link_basis":"first_harvest_node","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":"e1e8244b962d9c28"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}