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ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Emotional Reaction Intensity Estimation Challenges

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arxiv 2303.01498 v3 pith:5G647TI4 submitted 2023-03-02 cs.CV cs.LG

classification cs.CVcs.LG
keywords abawcompetitionemotionalestimationactionchallengescvprexpression
verification ladder T0 review T1 audit T2 compute T3 formal
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The fifth Affective Behavior Analysis in-the-wild (ABAW) Competition is part of the respective ABAW Workshop which will be held in conjunction with IEEE Computer Vision and Pattern Recognition Conference (CVPR), 2023. The 5th ABAW Competition is a continuation of the Competitions held at ECCV 2022, IEEE CVPR 2022, ICCV 2021, IEEE FG 2020 and CVPR 2017 Conferences, and is dedicated at automatically analyzing affect. For this year's Competition, we feature two corpora: i) an extended version of the Aff-Wild2 database and ii) the Hume-Reaction dataset. The former database is an audiovisual one of around 600 videos of around 3M frames and is annotated with respect to:a) two continuous affect dimensions -valence (how positive/negative a person is) and arousal (how active/passive a person is)-; b) basic expressions (e.g. happiness, sadness, neutral state); and c) atomic facial muscle actions (i.e., action units). The latter dataset is an audiovisual one in which reactions of individuals to emotional stimuli have been annotated with respect to seven emotional expression intensities. Thus the 5th ABAW Competition encompasses four Challenges: i) uni-task Valence-Arousal Estimation, ii) uni-task Expression Classification, iii) uni-task Action Unit Detection, and iv) Emotional Reaction Intensity Estimation. In this paper, we present these Challenges, along with their corpora, we outline the evaluation metrics, we present the baseline systems and illustrate their obtained performance.

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  1. TellTale: Blending Multi-Instance LoRA Text Encoders and a Zero-Shot LLM Judge for Ambivalence/Hesitancy Recognition in Videos

    cs.CV 2026-07 conditional novelty 4.0 of 10

    A text-only ensemble of two LoRA-tuned MIL encoders and a zero-shot LLM judge achieves Macro-F1 0.7364 on the BAH ambivalence/hesitancy challenge.

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