# Towards More Practical Group Activity Detection: A New Benchmark and Model

arXiv: https://arxiv.org/abs/2312.02878 · HTML: https://mlsanity.com/paper/2312.02878/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 62 references that are in the map.
- First posted on arXiv 5 Dec 2023.

## Builds on (most cited first, 30 of 38)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://mlsanity.com/paper/1608.00859/) (2016)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [DN-DETR: Accelerate DETR Training by Introducing Query DeNoising](https://mlsanity.com/paper/2203.01305/) (2022)
- [CrowdHuman: A Benchmark for Detecting Human in a Crowd](https://mlsanity.com/paper/1805.00123/) (2018)
- [CityPersons: A Diverse Dataset for Pedestrian Detection](https://mlsanity.com/paper/1702.05693/) (2017)
- [MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking](https://mlsanity.com/paper/2010.07548/) (2020)
- [Hierarchical Deep Temporal Models for Group Activity Recognition](https://mlsanity.com/paper/1607.02643/) (2016)
- [Actor-Transformers for Group Activity Recognition](https://mlsanity.com/paper/2003.12737/) (2020)
- [Learning Actor Relation Graphs for Group Activity Recognition](https://mlsanity.com/paper/1904.10117/) (2019)
- [GroupFormer: Group Activity Recognition with Clustered Spatial-Temporal Transformer](https://mlsanity.com/paper/2108.12630/) (2021)
- [UniPose: Unified Human Pose Estimation in Single Images and Videos](https://mlsanity.com/paper/2001.08095/) (2020)
- [Social Scene Understanding: End-to-End Multi-Person Action Localization and Collective Activity Recognition](https://mlsanity.com/paper/1611.09078/) (2016)
- [Social Adaptive Module for Weakly-supervised Group Activity Recognition](https://mlsanity.com/paper/2007.09470/) (2020)
- [Spatio-Temporal Dynamic Inference Network for Group Activity Recognition](https://mlsanity.com/paper/2108.11743/) (2021)
- [Joint Learning of Social Groups, Individuals Action and Sub-group Activities in Videos](https://mlsanity.com/paper/2007.02632/) (2020)
- [JRDB-Act: A Large-scale Dataset for Spatio-temporal Action, Social Group and Activity Detection](https://mlsanity.com/paper/2106.08827/) (2021)
- [Convolutional Relational Machine for Group Activity Recognition](https://mlsanity.com/paper/1904.03308/) (2019)

## Built on by (most cited first, 11 of 11)

- [GSON: A Group-based Social Navigation Framework with Large Multimodal Model](https://mlsanity.com/paper/2409.18084/) (2024)
- [SGA-INTERACT: A 3D Skeleton-based Benchmark for Group Activity Understanding in Modern Basketball Tactic](https://mlsanity.com/paper/2503.06522/) (2025)
- [Dynamic Group Detection using VLM-augmented Temporal Groupness Graph](https://mlsanity.com/paper/2509.04758/) (2025)
- [Pixels or Positions? Benchmarking Modalities in Group Activity Recognition](https://mlsanity.com/paper/2511.12606/) (2025)
- [Structured Relational Reasoning for Group Activity Assessment](https://mlsanity.com/paper/2508.07996/) (2025)
- [MPT-PAR:Mix-Parameters Transformer for Panoramic Activity Recognition](https://mlsanity.com/paper/2408.00420/) (2024)
- [Part-Aware Bottom-Up Group Reasoning for Fine-Grained Social Interaction Detection](https://mlsanity.com/paper/2511.03666/) (2025)
- [Human-in-the-loop Adaptation in Group Activity Feature Learning for Team Sports Video Retrieval](https://mlsanity.com/paper/2602.03157/) (2026)
- [EgoGroups: A Benchmark For Detecting Social Groups of People in the Wild](https://mlsanity.com/paper/2603.22249/) (2026)
- [End-to-End Shared Attention Estimation via Group Detection with Feedback Refinement](https://mlsanity.com/paper/2604.01714/) (2026)
- [Where Should I Join? Robot Group Joining via Language-Guided Goal Prediction](https://mlsanity.com/paper/2609.28467/) (2026)

## Abstract (arXiv)

Group activity detection (GAD) is the task of identifying members of each group and classifying the activity of the group at the same time in a video. While GAD has been studied recently, there is still much room for improvement in both dataset and methodology due to their limited capability to address practical GAD scenarios. To resolve these issues, we first present a new dataset, dubbed Caf'e. Unlike existing datasets, Caf'e is constructed primarily for GAD and presents more practical scenarios and metrics, as well as being large-scale and providing rich annotations. Along with the dataset, we propose a new GAD model that deals with an unknown number of groups and latent group members efficiently and effectively. We evaluated our model on three datasets including Caf'e, where it outperformed previous work in terms of both accuracy and inference speed.