# Social Adaptive Module for Weakly-supervised Group Activity Recognition

arXiv: https://arxiv.org/abs/2007.09470 · HTML: https://mlsanity.com/paper/2007.09470/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2020.

## Built on by (most cited first, 30 of 42)

- [Spatio-Temporal Dynamic Inference Network for Group Activity Recognition](https://mlsanity.com/paper/2108.11743/) (2021)
- [M3Net: Multi-view Encoding, Matching, and Fusion for Few-shot Fine-grained Action Recognition](https://mlsanity.com/paper/2308.03063/) (2023)
- [Dual-AI: Dual-path Actor Interaction Learning for Group Activity Recognition](https://mlsanity.com/paper/2204.02148/) (2022)
- [AVA-AVD: Audio-Visual Speaker Diarization in the Wild](https://mlsanity.com/paper/2111.14448/) (2021)
- [Hunting Group Clues with Transformers for Social Group Activity Recognition](https://mlsanity.com/paper/2207.05254/) (2022)
- [Detector-Free Weakly Supervised Group Activity Recognition](https://mlsanity.com/paper/2204.02139/) (2022)
- [COMPOSER: Compositional Reasoning of Group Activity in Videos with Keypoint-Only Modality](https://mlsanity.com/paper/2112.05892/) (2021)
- [BASKET: A Large-Scale Video Dataset for Fine-Grained Skill Estimation](https://mlsanity.com/paper/2503.20781/) (2025)
- [SportsHHI: A Dataset for Human-Human Interaction Detection in Sports Videos](https://mlsanity.com/paper/2404.04565/) (2024)
- [SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition](https://mlsanity.com/paper/2303.12149/) (2023)
- [Continual Learning for Image Segmentation with Dynamic Query](https://mlsanity.com/paper/2311.17450/) (2023)
- [A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision](https://mlsanity.com/paper/2307.03353/) (2023)
- [Towards More Practical Group Activity Detection: A New Benchmark and Model](https://mlsanity.com/paper/2312.02878/) (2023)
- [ERA: Expert Retrieval and Assembly for Early Action Prediction](https://mlsanity.com/paper/2207.09675/) (2022)
- [Graph Reasoning Transformer for Image Parsing](https://mlsanity.com/paper/2209.09545/) (2022)
- [Widely Applicable Strong Baseline for Sports Ball Detection and Tracking](https://mlsanity.com/paper/2311.05237/) (2023)
- [A Survey of Video Datasets for Grounded Event Understanding](https://mlsanity.com/paper/2406.09646/) (2024)
- [SoGAR: Self-supervised Spatiotemporal Attention-based Social Group Activity Recognition](https://mlsanity.com/paper/2305.06310/) (2023)
- [Self-supervised Social Relation Representation for Human Group Detection](https://mlsanity.com/paper/2203.03843/) (2022)
- [Skeleton-based Group Activity Recognition via Spatial-Temporal Panoramic Graph](https://mlsanity.com/paper/2407.19497/) (2024)
- [REACT: Recognize Every Action Everywhere All At Once](https://mlsanity.com/paper/2312.00188/) (2023)
- [Learning Human-Object Interaction as Groups](https://mlsanity.com/paper/2510.18357/) (2025)
- [Relational Reasoning Networks](https://mlsanity.com/paper/2106.00393/) (2021)
- [Flow-Assisted Motion Learning Network for Weakly-Supervised Group Activity Recognition](https://mlsanity.com/paper/2405.18012/) (2024)
- [SGA-INTERACT: A 3D Skeleton-based Benchmark for Group Activity Understanding in Modern Basketball Tactic](https://mlsanity.com/paper/2503.06522/) (2025)
- [Coupling Global Context and Local Contents for Weakly-Supervised Semantic Segmentation](https://mlsanity.com/paper/2304.09059/) (2023)
- [Knowledge Guided Entity-aware Video Captioning and A Basketball Benchmark](https://mlsanity.com/paper/2401.13888/) (2024)
- [Learning Group Activity Features Through Person Attribute Prediction](https://mlsanity.com/paper/2403.02753/) (2024)
- [Self-supervised Multi-actor Social Activity Understanding in Streaming Videos](https://mlsanity.com/paper/2406.14472/) (2024)
- [Pixels or Positions? Benchmarking Modalities in Group Activity Recognition](https://mlsanity.com/paper/2511.12606/) (2025)

## Abstract (arXiv)

This paper presents a new task named weakly-supervised group activity recognition (GAR) which differs from conventional GAR tasks in that only video-level labels are available, yet the important persons within each frame are not provided even in the training data. This eases us to collect and annotate a large-scale NBA dataset and thus raise new challenges to GAR. To mine useful information from weak supervision, we present a key insight that key instances are likely to be related to each other, and thus design a social adaptive module (SAM) to reason about key persons and frames from noisy data. Experiments show significant improvement on the NBA dataset as well as the popular volleyball dataset. In particular, our model trained on video-level annotation achieves comparable accuracy to prior algorithms which required strong labels.