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Behaviour4All: in-the-wild Facial Behaviour Analysis Toolkit

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arxiv 2409.17717 v1 pith:R2RA27II submitted 2024-09-26 cs.CV

classification cs.CV
keywords behavior4allin-the-wildanalysisdatabasesexpressionfacialframeworkleverages
verification ladder T0 review T1 audit T2 compute T3 formal
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In this paper, we introduce Behavior4All, a comprehensive, open-source toolkit for in-the-wild facial behavior analysis, integrating Face Localization, Valence-Arousal Estimation, Basic Expression Recognition and Action Unit Detection, all within a single framework. Available in both CPU-only and GPU-accelerated versions, Behavior4All leverages 12 large-scale, in-the-wild datasets consisting of over 5 million images from diverse demographic groups. It introduces a novel framework that leverages distribution matching and label co-annotation to address tasks with non-overlapping annotations, encoding prior knowledge of their relatedness. In the largest study of its kind, Behavior4All outperforms both state-of-the-art and toolkits in overall performance as well as fairness across all databases and tasks. It also demonstrates superior generalizability on unseen databases and on compound expression recognition. Finally, Behavior4All is way times faster than other toolkits.

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Forward citations

Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DVD: A Comprehensive Dataset for Advancing Violence Detection in Real-World Scenarios

    cs.CV 2025-05 conditional novelty 6.0 of 10

    The authors propose a new frame-level annotated violence detection dataset, DVD, with 500 videos and rich metadata, but it is not yet available and lacks validation experiments.

  2. AffectFlow-DINO: Uncertainty-Aware Multi-Task Affect Estimation via Conditional Rectified Flow

    cs.CV 2026-07 conditional novelty 5.0 of 10

    Adding a conditional rectified-flow head to a DINOv3 multi-task affect model improves valence-arousal CCC by +0.058 when the backbone is frozen and, with fine-tuning and validation-tuned calibration, reaches P_MTL=1.1...

  3. Causal Supervision of Attention for Affective Behaviour Analysis

    cs.CV 2026-07 unverdicted novelty 5.0 of 10

    Causal supervision plus K-V independence and SwiGLU attention pooling yields a multi-task P-score of 1.2214 on s-Aff-Wild2 validation for valence-arousal, expression, and action-unit prediction.

  4. Strength-Parity Ensembling with Parameter-Isolated Experts for Multi-Task Affect Recognition

    cs.CV 2026-07 conditional novelty 5.0 of 10

    Parameter-isolated LoRA experts on one face backbone stay decorrelated (0.91 vs 0.98 for full fine-tuning) and improve an ABAW affect ensemble from 1.6669 to 1.6949–1.7259 validation score.

  5. A Shared Latent for Partially-Labeled Multi-Task Facial Affect Recognition

    cs.CV 2026-07 accept novelty 5.0 of 10

    A shared variational affect latent that marginalizes missing labels lifts rare expression and action-unit recognition on s-Aff-Wild2 beyond masked-loss training.

  6. Distance-aware Soft Prompt Guidance for Multimodal Valence-Arousal Estimation

    cs.CV 2026-03 reject novelty 5.0 of 10

    Distance-aware soft prompts over a 3×3 emotion grid with CLIP text prototypes and audio-visual GRU fusion achieve CCC_mean 0.5361 on Aff-Wild2, beating only the paper's self-defined baselines.

  7. AffectFuse: Cross-Task Feature Fusion with Temporal Modeling for Multi-Task Affective Behavior Analysis

    cs.CV 2026-07 conditional novelty 4.0 of 10

    A multi-task affective system combining frozen AffectNet backbones, LoRA-adapted MAE for action units, temporal heads, fusion, and ensembling attains P=1.7302 on the s-Aff-Wild2 validation split.

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