# LiGAR: LiDAR-Guided Hierarchical Transformer for Multi-Modal Group Activity Recognition

arXiv: https://arxiv.org/abs/2410.21108 · HTML: https://mlsanity.com/paper/2410.21108/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 48 references that are in the map.
- First posted on arXiv 28 Oct 2024.

## Builds on (most cited first, 30 of 31)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [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)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [Open3D: A Modern Library for 3D Data Processing](https://mlsanity.com/paper/1801.09847/) (2018)
- [NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis](https://mlsanity.com/paper/1604.02808/) (2016)
- [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)
- [MSMDFusion: Fusing LiDAR and Camera at Multiple Scales with Multi-Depth Seeds for 3D Object Detection](https://mlsanity.com/paper/2209.03102/) (2022)
- [GroupFormer: Group Activity Recognition with Clustered Spatial-Temporal Transformer](https://mlsanity.com/paper/2108.12630/) (2021)
- [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)
- [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)
- [Hierarchical Long Short-Term Concurrent Memory for Human Interaction Recognition](https://mlsanity.com/paper/1811.00270/) (2018)
- [Structure Inference Machines: Recurrent Neural Networks for Analyzing Relations in Group Activity Recognition](https://mlsanity.com/paper/1511.04196/) (2015)
- [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)
- [Panoramic Human Activity Recognition](https://mlsanity.com/paper/2203.03806/) (2022)
- [X-3D: Explicit 3D Structure Modeling for Point Cloud Recognition](https://mlsanity.com/paper/2404.15010/) (2024)
- [SoGAR: Self-supervised Spatiotemporal Attention-based Social Group Activity Recognition](https://mlsanity.com/paper/2305.06310/) (2023)
- [REACT: Recognize Every Action Everywhere All At Once](https://mlsanity.com/paper/2312.00188/) (2023)
- [HAtt-Flow: Hierarchical Attention-Flow Mechanism for Group Activity Scene Graph Generation in Videos](https://mlsanity.com/paper/2312.07740/) (2023)

## Built on by (most cited first, 1 of 1)

- [Group-DINOmics: Incorporating People Dynamics into DINO for Self-supervised Group Activity Feature Learning](https://mlsanity.com/paper/2604.04467/) (2026)

## Abstract (arXiv)

Group Activity Recognition (GAR) remains challenging in computer vision due to the complex nature of multi-agent interactions. This paper introduces LiGAR, a LIDAR-Guided Hierarchical Transformer for Multi-Modal Group Activity Recognition. LiGAR leverages LiDAR data as a structural backbone to guide the processing of visual and textual information, enabling robust handling of occlusions and complex spatial arrangements. Our framework incorporates a Multi-Scale LIDAR Transformer, Cross-Modal Guided Attention, and an Adaptive Fusion Module to integrate multi-modal data at different semantic levels effectively. LiGAR's hierarchical architecture captures group activities at various granularities, from individual actions to scene-level dynamics. Extensive experiments on the JRDB-PAR, Volleyball, and NBA datasets demonstrate LiGAR's superior performance, achieving state-of-the-art results with improvements of up to 10.6% in F1-score on JRDB-PAR and 5.9% in Mean Per Class Accuracy on the NBA dataset. Notably, LiGAR maintains high performance even when LiDAR data is unavailable during inference, showcasing its adaptability. Our ablation studies highlight the significant contributions of each component and the effectiveness of our multi-modal, multi-scale approach in advancing the field of group activity recognition.