# Convolutional Relational Machine for Group Activity Recognition

arXiv: https://arxiv.org/abs/1904.03308 · HTML: https://mlsanity.com/paper/1904.03308/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2019.

## Built on by (most cited first, 30 of 34)

- [GroupFormer: Group Activity Recognition with Clustered Spatial-Temporal Transformer](https://mlsanity.com/paper/2108.12630/) (2021)
- [Spatio-Temporal Dynamic Inference Network for Group Activity Recognition](https://mlsanity.com/paper/2108.11743/) (2021)
- [Dual-AI: Dual-path Actor Interaction Learning for Group Activity Recognition](https://mlsanity.com/paper/2204.02148/) (2022)
- [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)
- [Pose is all you need: The pose only group activity recognition system (POGARS)](https://mlsanity.com/paper/2108.04186/) (2021)
- [SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition](https://mlsanity.com/paper/2303.12149/) (2023)
- [Towards More Practical Group Activity Detection: A New Benchmark and Model](https://mlsanity.com/paper/2312.02878/) (2023)
- [Keypoint Communities](https://mlsanity.com/paper/2110.00988/) (2021)
- [SoGAR: Self-supervised Spatiotemporal Attention-based Social Group Activity Recognition](https://mlsanity.com/paper/2305.06310/) (2023)
- [Entry-Flipped Transformer for Inference and Prediction of Participant Behavior](https://mlsanity.com/paper/2207.06235/) (2022)
- [Skeleton-based Group Activity Recognition via Spatial-Temporal Panoramic Graph](https://mlsanity.com/paper/2407.19497/) (2024)
- [Game State and Spatio-temporal Action Detection in Soccer using Graph Neural Networks and 3D Convolutional Networks](https://mlsanity.com/paper/2502.15462/) (2025)
- [REACT: Recognize Every Action Everywhere All At Once](https://mlsanity.com/paper/2312.00188/) (2023)
- [Learning Group Activities from Skeletons without Individual Action Labels](https://mlsanity.com/paper/2105.06754/) (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)
- [CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action Recognition](https://mlsanity.com/paper/2410.07153/) (2024)
- [Knowledge Augmented Relation Inference for Group Activity Recognition](https://mlsanity.com/paper/2302.14350/) (2023)
- [DECOMPL: Decompositional Learning with Attention Pooling for Group Activity Recognition from a Single Volleyball Image](https://mlsanity.com/paper/2303.06439/) (2023)
- [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)
- [Group Activity Recognition using Unreliable Tracked Pose](https://mlsanity.com/paper/2401.03262/) (2024)
- [Design and Analysis of Efficient Attention in Transformers for Social Group Activity Recognition](https://mlsanity.com/paper/2404.09964/) (2024)
- [Group Activity Recognition via Dynamic Composition and Interaction](https://mlsanity.com/paper/2305.05583/) (2023)
- [Group Activity Recognition in Computer Vision: A Comprehensive Review, Challenges, and Future Perspectives](https://mlsanity.com/paper/2307.13541/) (2023)
- [VicKAM: Visual Conceptual Knowledge Guided Action Map for Weakly Supervised Group Activity Recognition](https://mlsanity.com/paper/2502.09967/) (2025)
- [PromptGAR: Flexible Promptive Group Activity Recognition](https://mlsanity.com/paper/2503.08933/) (2025)
- [Multi-Focused Video Group Activities Hashing](https://mlsanity.com/paper/2509.00490/) (2025)

## Abstract (arXiv)

We present an end-to-end deep Convolutional Neural Network called Convolutional Relational Machine (CRM) for recognizing group activities that utilizes the information in spatial relations between individual persons in image or video. It learns to produce an intermediate spatial representation (activity map) based on individual and group activities. A multi-stage refinement component is responsible for decreasing the incorrect predictions in the activity map. Finally, an aggregation component uses the refined information to recognize group activities. Experimental results demonstrate the constructive contribution of the information extracted and represented in the form of the activity map. CRM shows advantages over state-of-the-art models on Volleyball and Collective Activity datasets.