# From Affect to Complex Behavior: Advancing Multimodal Human-Centered AI at the 10th ABAW Workshop & Competition

arXiv: https://arxiv.org/abs/2605.27451 · HTML: https://mlsanity.com/paper/2605.27451/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 22 references that are in the map.
- First posted on arXiv 24 May 2026.

## Builds on (most cited first, 18 of 18)

- [Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond](https://mlsanity.com/paper/1804.10938/) (2018)
- [MixAugment & Mixup: Augmentation Methods for Facial Expression Recognition](https://mlsanity.com/paper/2205.04442/) (2022)
- [Behaviour4All: in-the-wild Facial Behaviour Analysis Toolkit](https://mlsanity.com/paper/2409.17717/) (2024)
- [DVD: A Comprehensive Dataset for Advancing Violence Detection in Real-World Scenarios](https://mlsanity.com/paper/2506.05372/) (2025)
- [Conflict-Aware Multimodal Fusion for Ambivalence and Hesitancy Recognition](https://mlsanity.com/paper/2603.15818/) (2026)
- [BROTHER: Behavioral Recognition Optimized Through Heterogeneous Ensemble Regularization for Ambivalence and Hesitancy](https://mlsanity.com/paper/2603.14361/) (2026)
- [Team LEYA in 10th ABAW Competition: Multimodal Ambivalence/Hesitancy Recognition Approach](https://mlsanity.com/paper/2603.12848/) (2026)
- [Solution for 10th Competition on Ambivalence/Hesitancy (AH) Video Recognition Challenge using Divergence-Based Multimodal Fusion](https://mlsanity.com/paper/2603.16939/) (2026)
- [Nuanced Emotion Recognition Based on a Segment-based MLLM Framework Leveraging Qwen3-Omni for AH Detection](https://mlsanity.com/paper/2603.13406/) (2026)
- [Multimodal Emotion Regression with Multi-Objective Optimization and VAD-Aware Audio Modeling for the 10th ABAW EMI Track](https://mlsanity.com/paper/2603.13760/) (2026)
- [Solution to the 10th ABAW Expression Recognition Challenge: A Robust Multimodal Framework with Safe Cross-Attention and Modality Dropout](https://mlsanity.com/paper/2603.08034/) (2026)
- [Hierarchical Granularity Alignment and State Space Modeling for Robust Multimodal AU Detection in the Wild](https://mlsanity.com/paper/2603.11306/) (2026)
- [Stage-Adaptive Reliability Modeling for Continuous Valence-Arousal Estimation](https://mlsanity.com/paper/2603.11468/) (2026)
- [Multimodal Emotion Recognition via Bi-directional Cross-Attention and Temporal Modeling](https://mlsanity.com/paper/2603.11971/) (2026)
- [A Two-Stage Dual-Modality Model for Facial Emotional Expression Recognition](https://mlsanity.com/paper/2603.12221/) (2026)
- [HSEmotion Team at ABAW-10 Competition: Facial Expression Recognition, Valence-Arousal Estimation, Action Unit Detection and Fine-Grained Violence Classification](https://mlsanity.com/paper/2603.12693/) (2026)
- [Team RAS in 10th ABAW Competition: Multimodal Valence and Arousal Estimation Approach](https://mlsanity.com/paper/2603.13056/) (2026)
- [Distance-aware Soft Prompt Guidance for Multimodal Valence-Arousal Estimation](https://mlsanity.com/paper/2603.13415/) (2026)

## Built on by (most cited first, 12 of 12)

- [HSEmotion Team at the 11th ABAW Challenge: Multi-Task Learning and Ambivalence/Hesitancy Video Recognition](https://mlsanity.com/paper/2607.12774/) (2026)
- [A Shared Latent for Partially-Labeled Multi-Task Facial Affect Recognition](https://mlsanity.com/paper/2607.16285/) (2026)
- [Causal Supervision of Attention for Affective Behaviour Analysis](https://mlsanity.com/paper/2607.12091/) (2026)
- [HEDGE: A Calibrated Ensemble for A/H Recognition](https://mlsanity.com/paper/2607.12176/) (2026)
- [AffectFlow-DINO: Uncertainty-Aware Multi-Task Affect Estimation via Conditional Rectified Flow](https://mlsanity.com/paper/2607.13250/) (2026)
- [Audio-Text Cross-Attention with Psycholinguistic Support Features for Ambivalence/Hesitancy Recognition](https://mlsanity.com/paper/2607.13345/) (2026)
- [CF-Net: Conflict Fusion with Speaker Normalisation and Certainty Weighting for Ambivalence/Hesitancy Recognition](https://mlsanity.com/paper/2607.13976/) (2026)
- [Multimodal Ambivalence and Hesitancy Recognition via Cross-Attention and Gated Fusion](https://mlsanity.com/paper/2607.15779/) (2026)
- [Strength-Parity Ensembling with Parameter-Isolated Experts for Multi-Task Affect Recognition](https://mlsanity.com/paper/2607.16290/) (2026)
- [AffectFuse: Cross-Task Feature Fusion with Temporal Modeling for Multi-Task Affective Behavior Analysis](https://mlsanity.com/paper/2607.16546/) (2026)
- [TellTale: Blending Multi-Instance LoRA Text Encoders and a Zero-Shot LLM Judge for Ambivalence/Hesitancy Recognition in Videos](https://mlsanity.com/paper/2607.16635/) (2026)
- [Modality Discrepancy Transformer for Ambivalence and Hesitancy Recognition](https://mlsanity.com/paper/2609.19148/) (2026)

## Abstract (arXiv)

The 10th Affective & Behavior Analysis in-the-Wild (ABAW) Workshop and Competition, held at CVPR 2026, continues to advance research on modelling, analysis, understanding of human affect and behavior in real-world, unconstrained environments. The workshop maintains its dual structure, comprising both a competition and a paper track. The ABAW Competition introduces a diverse set of challenges targeting key aspects of affective and behavioral understanding, including continuous affect (valence-arousal) estimation, discrete affect (expression and action unit) recognition, as well as more complex behavior analysis tasks, such as emotional mimicry intensity estimation, ambivalence/hesitancy recognition and fine-grained violence detection. These challenges are built upon large-scale in-the-wild datasets, providing comprehensive benchmarks for state-of-the-art approaches. In parallel, the paper track presents a wide range of contributions spanning pose, motion & behavior estimation, affect modelling & multimodal learning, benchmarks, datasets & evaluation protocols, fairness, robustness & deployment. Overall, the 10th ABAW Workshop and Competition continues to serve as a key platform for benchmarking, collaboration and innovation, shaping the development of next-generation multimodal, human-centered AI systems.