# GAViD: A Large-Scale Multimodal Dataset for Context-Aware Group Affect Recognition from Videos

arXiv: https://arxiv.org/abs/2604.16214 · HTML: https://mlsanity.com/paper/2604.16214/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 69 references that are in the map.
- First posted on arXiv 17 Apr 2026.

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

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models](https://mlsanity.com/paper/2306.05424/) (2023)
- [SALSA: A Novel Dataset for Multimodal Group Behavior Analysis](https://mlsanity.com/paper/1506.06882/) (2015)
- [FindingEmo: An Image Dataset for Emotion Recognition in the Wild](https://mlsanity.com/paper/2402.01355/) (2024)
- [EmotiW 2018: Audio-Video, Student Engagement and Group-Level Affect Prediction](https://mlsanity.com/paper/1808.07773/) (2018)
- [Non-Volume Preserving-based Fusion to Group-Level Emotion Recognition on Crowd Videos](https://mlsanity.com/paper/1811.11849/) (2018)
- [VEATIC: Video-based Emotion and Affect Tracking in Context Dataset](https://mlsanity.com/paper/2309.06745/) (2023)
- [Graph Neural Networks for Image Understanding Based on Multiple Cues: Group Emotion Recognition and Event Recognition as Use Cases](https://mlsanity.com/paper/1909.12911/) (2019)
- [Video-GPT via Next Clip Diffusion](https://mlsanity.com/paper/2505.12489/) (2025)
- [Towards A Robust Group-level Emotion Recognition via Uncertainty-Aware Learning](https://mlsanity.com/paper/2310.04306/) (2023)
- [Computational Analysis of Stress, Depression and Engagement in Mental Health: A Survey](https://mlsanity.com/paper/2403.08824/) (2024)
- [Group-Level Emotion Recognition Using a Unimodal Privacy-Safe Non-Individual Approach](https://mlsanity.com/paper/2009.07013/) (2020)
- [Multimodal Group Emotion Recognition In-the-wild Using Privacy-Compliant Features](https://mlsanity.com/paper/2312.05265/) (2023)
- [A Survey of Deep Learning for Group-level Emotion Recognition](https://mlsanity.com/paper/2408.15276/) (2024)
- [Explainable Human-centered Traits from Head Motion and Facial Expression Dynamics](https://mlsanity.com/paper/2302.09817/) (2023)

## Abstract (arXiv)

Understanding affective dynamics in real-world social systems is fundamental to modeling and analyzing human-human interactions in complex environments. Group affect emerges from intertwined human-human interactions, contextual influences, and behavioral cues, making its quantitative modeling a challenging computational social systems problem. However, computational modeling of group affect in in-the-wild scenarios remains challenging due to limited large-scale annotated datasets and the inherent complexity of multimodal social interactions shaped by contextual and behavioral variability. The lack of comprehensive datasets annotated with multimodal and contextual information further limits advances in the field. To address this, we introduce the Group Affect from ViDeos (GAViD) dataset, comprising 5091 video clips with multimodal data (video, audio and context), annotated with ternary valence and discrete emotion labels and enriched with VideoGPT-generated contextual metadata and human-annotated action cues. We also present Context-Aware Group Affect Recognition Network (CAGNet) for multimodal context-aware group affect recognition. CAGNet achieves 63.20% test accuracy on GAViD, comparable to state-of-the-art performance. The dataset and code are available at github.com/deepakkumar-iitr/GAViD.