Pith. sign in

REVIEW 9 cited by

Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Framework

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2103.15792 v1 pith:RNHVV45X submitted 2021-03-29 cs.CV cs.AIcs.LG

Dimitrios Kollias , Stefanos Zafeiriou This is my paper
classification cs.CVcs.AIcs.LG
keywords affectrecognitiondatabasesin-the-wildanalysislargedeepexpressions
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Affect recognition based on subjects' facial expressions has been a topic of major research in the attempt to generate machines that can understand the way subjects feel, act and react. In the past, due to the unavailability of large amounts of data captured in real-life situations, research has mainly focused on controlled environments. However, recently, social media and platforms have been widely used. Moreover, deep learning has emerged as a means to solve visual analysis and recognition problems. This paper exploits these advances and presents significant contributions for affect analysis and recognition in-the-wild. Affect analysis and recognition can be seen as a dual knowledge generation problem, involving: i) creation of new, large and rich in-the-wild databases and ii) design and training of novel deep neural architectures that are able to analyse affect over these databases and to successfully generalise their performance on other datasets. The paper focuses on large in-the-wild databases, i.e., Aff-Wild and Aff-Wild2 and presents the design of two classes of deep neural networks trained with these databases. The first class refers to uni-task affect recognition, focusing on prediction of the valence and arousal dimensional variables. The second class refers to estimation of all main behavior tasks, i.e. valence-arousal prediction; categorical emotion classification in seven basic facial expressions; facial Action Unit detection. A novel multi-task and holistic framework is presented which is able to jointly learn and effectively generalize and perform affect recognition over all existing in-the-wild databases. Large experimental studies illustrate the achieved performance improvement over the existing state-of-the-art in affect recognition.

Discussion (0). Sign in to comment.

Forward citations

Cited by 9 Pith papers

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

  1. 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...

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. Team RAS in 9th ABAW Competition: Multimodal Compound Expression Recognition Approach

    cs.CV 2025-07 conditional novelty 5.0 of 10

    A six-modality zero-shot pipeline with CLIP, Qwen-VL, WavLM, Mamba, and new fusion/aggregation modules reports F1 scores of 46.95 (AffWild2), 49.02 (AFEW), and 34.85 (C-EXPR-DB) without target-domain fine-tuning.

  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.

  8. Multimodal Alignment with Cross-Attentive GRUs for Fine-Grained Video Understanding

    cs.CV 2025-07 reject novelty 4.0 of 10

    A GRU-based cross-attention fusion of frozen vision-language encoders is claimed to achieve strong results on DVD and Aff-Wild2, but the supporting experiments are missing from the paper.

  9. TAGF: Time-aware Gated Fusion for Multimodal Valence-Arousal Estimation

    cs.MM 2025-07 reject novelty 4.0 of 10

    TAGF adds a BiLSTM-based gate that reweights recursive cross-attention outputs for valence-arousal prediction, with results slightly below several existing methods on Aff-Wild2.

Pith tools