# Context-Aware Mixture of Domain Experts for Bodily Expression of Emotion in the Wild

arXiv: https://arxiv.org/abs/2608.02331 · HTML: https://mlsanity.com/paper/2608.02331/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 25 references that are in the map.
- First posted on arXiv 3 Aug 2026.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions](https://mlsanity.com/paper/1705.08421/) (2017)
- [EVA-02: A Visual Representation for Neon Genesis](https://mlsanity.com/paper/2303.11331/) (2023)
- [Context Based Emotion Recognition using EMOTIC Dataset](https://mlsanity.com/paper/2003.13401/) (2020)
- [Unsupervised Person Re-identification by Soft Multilabel Learning](https://mlsanity.com/paper/1903.06325/) (2019)
- [Going Deeper in Facial Expression Recognition using Deep Neural Networks](https://mlsanity.com/paper/1511.04110/) (2015)
- [ARBEE: Towards Automated Recognition of Bodily Expression of Emotion In the Wild](https://mlsanity.com/paper/1808.09568/) (2018)
- [Learning Emotion Representations from Verbal and Nonverbal Communication](https://mlsanity.com/paper/2305.13500/) (2023)
- [VLLMs Provide Better Context for Emotion Understanding Through Common Sense Reasoning](https://mlsanity.com/paper/2404.07078/) (2024)
- [Emotion Understanding in Videos Through Body, Context, and Visual-Semantic Embedding Loss](https://mlsanity.com/paper/2010.16396/) (2020)
- [Leveraging Semantic Scene Characteristics and Multi-Stream Convolutional Architectures in a Contextual Approach for Video-Based Visual Emotion Recognition in the Wild](https://mlsanity.com/paper/2105.07484/) (2021)
- [Pain level and pain-related behaviour classification using GRU-based sparsely-connected RNNs](https://mlsanity.com/paper/2212.14806/) (2022)
- [SoniWeight Shoes: Investigating Effects and Personalization of a Wearable Sound Device for Altering Body Perception and Behavior](https://mlsanity.com/paper/2403.06651/) (2024)
- [L-SFAN: Lightweight Spatially-focused Attention Network for Pain Behavior Detection](https://mlsanity.com/paper/2406.16913/) (2024)

## Abstract (arXiv)

The same body posture can convey entirely different emotions depending on its surrounding context, yet most methods for recognising bodily emotions treat scene and object cues as auxiliary feature augmentations rather than as structured priors over the plausibility of emotions. We introduce the Context-Aware Mixture of Domain Experts (CA-MoDE) for bodily emotion recognition. CA-MoDE incorporates dedicated scene and object experts to generate soft distributions over emotion categories conditioned on their respective domains. These domain-conditioned soft predictions serve as structured contextual priors that modulate the body expert's predictions at the distributional level rather than at the feature level. To fuse these multi-domain signals, we propose a task-tailored max-endorsement gating strategy that selects the strongest contextual signal across experts for each emotion dimension. Our gating strategy mitigates the signal dilution that typically occurs when conflicting or uninformative context distributions are averaged. CA-MoDE achieves an Emotion Recognition Score of 0.3269 on the Body Language Database. By outperforming existing temporal models using only single still images, our framework demonstrates that explicitly modelling structured spatial context can serve as a complementary discriminative proxy for the behavioural dynamics typically captured by video.