{"url":"/sota/emotion-classification-on-ewalk","task":{"name":"Emotion Classification","url":"/task/emotion-classification","note":null},"dataset":{"name":"EWALK","url":null},"category":"Computer Vision","categories":["Computer Vision","Natural Language Processing"],"category_note":null,"description":"Emotion classification, or emotion categorization, is the task of recognising emotions to classify them into the corresponding category. Given an input, classify it as 'neutral or no emotion' or as one, or more, of several given emotions that best represent the mental state of the subject's facial expression, words, and so on. Some example benchmarks include ROCStories, Many Faces of Anger (MFA), and GoEmotions. Models can be evaluated using metrics such as the Concordance Correlation Coefficient (CCC) and the Mean Squared Error (MSE).","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["Accuracy"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Accuracy":"higher"}},"counts":{"rows":3,"rows_with_code":3,"rows_with_paper_page":3,"rows_dated":3,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"ProxEmo (ours)","metrics":{"Accuracy":"82.4"},"uses_additional_data":false,"paper_date":"2020-03-02","paper":"/paper/proxemo-gait-based-emotion-learning-and-multi","paper_url":"https://arxiv.org/abs/2003.01062v2","paper_title":"ProxEmo: Gait-based Emotion Learning and Multi-view Proxemic Fusion for Socially-Aware Robot Navigation","code":"https://github.com/vijay4313/proxemo","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"STEP [bhattacharya2019step]","metrics":{"Accuracy":"78.24"},"uses_additional_data":false,"paper_date":"2020-03-02","paper":"/paper/proxemo-gait-based-emotion-learning-and-multi","paper_url":"https://arxiv.org/abs/2003.01062v2","paper_title":"ProxEmo: Gait-based Emotion Learning and Multi-view Proxemic Fusion for Socially-Aware Robot Navigation","code":"https://github.com/vijay4313/proxemo","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"Baseline (Vanilla LSTM) [Ewalk]","metrics":{"Accuracy":"55.47"},"uses_additional_data":false,"paper_date":"2020-03-02","paper":"/paper/proxemo-gait-based-emotion-learning-and-multi","paper_url":"https://arxiv.org/abs/2003.01062v2","paper_title":"ProxEmo: Gait-based Emotion Learning and Multi-view Proxemic Fusion for Socially-Aware Robot Navigation","code":"https://github.com/vijay4313/proxemo","n_code_links":1,"syntology":null}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}