693 citations · 2.2k across the 82 of their papers we have counts for
83 papers
A Markov Random Field model for Hypergraph-based Machine Learning
Bohan Tang, Keyue Jiang, Laura Toni +2
Understanding the data-generating process is essential for building machine learning models that generalise well while ensuring robustness and interpretability. This paper addresse…
Interruption-Aware Cooperative Perception for V2X Communication-Aided Autonomous Driving
Shunli Ren, Zixing Lei, Zi Wang +4
Cooperative perception can significantly improve the perception performance of autonomous vehicles beyond the limited perception ability of individual vehicles by exchanging inform…
EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoning
Chenxin Xu, Robby T. Tan, Yuhong Tan +4
Learning to predict agent motions with relationship reasoning is important for many applications. In motion prediction tasks, maintaining motion equivariance under Euclidean geomet…
Collaboration Helps Camera Overtake LiDAR in 3D Detection
Yue Hu, Yifan Lu, Runsheng Xu +3
Camera-only 3D detection provides an economical solution with a simple configuration for localizing objects in 3D space compared to LiDAR-based detection systems. However, a major…
TBP-Former: Learning Temporal Bird's-Eye-View Pyramid for Joint Perception and Prediction in Vision-Centric Autonomous Driving
Shaoheng Fang, Zi Wang, Yiqi Zhong +3
Vision-centric joint perception and prediction (PnP) has become an emerging trend in autonomous driving research. It predicts the future states of the traffic participants in the s…
Leapfrog Diffusion Model for Stochastic Trajectory Prediction
Weibo Mao, Chenxin Xu, Qi Zhu +2
To model the indeterminacy of human behaviors, stochastic trajectory prediction requires a sophisticated multi-modal distribution of future trajectories. Emerging diffusion models…
Among Us: Adversarially Robust Collaborative Perception by Consensus
Yiming Li, Qi Fang, Jiamu Bai +3
Multiple robots could perceive a scene (e.g., detect objects) collaboratively better than individuals, although easily suffer from adversarial attacks when using deep learning. Thi…
Discriminative Radial Domain Adaptation
Zenan Huang, Jun Wen, Siheng Chen +2
Domain adaptation methods reduce domain shift typically by learning domain-invariant features. Most existing methods are built on distribution matching, e.g., adversarial domain ad…
Robust Collaborative 3D Object Detection in Presence of Pose Errors
Yifan Lu, Quanhao Li, Baoan Liu +4
Collaborative 3D object detection exploits information exchange among multiple agents to enhance accuracy of object detection in presence of sensor impairments such as occlusion. H…
Learning Hypergraphs From Signals With Dual Smoothness Prior
Bohan Tang, Siheng Chen, Xiaowen Dong
Hypergraph structure learning, which aims to learn the hypergraph structures from the observed signals to capture the intrinsic high-order relationships among the entities, becomes…
Number-Adaptive Prototype Learning for 3D Point Cloud Semantic Segmentation
Yangheng Zhao, Jun Wang, Xiaolong Li +4
3D point cloud semantic segmentation is one of the fundamental tasks for 3D scene understanding and has been widely used in the metaverse applications. Many recent 3D semantic segm…
Unrolled Graph Learning for Multi-Agent Collaboration
Enpei Zhang, Shuo Tang, Xiaowen Dong +2
Multi-agent learning has gained increasing attention to tackle distributed machine learning scenarios under constrictions of data exchanging. However, existing multi-agent learning…
Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps
Yue Hu, Shaoheng Fang, Zixing Lei +2
Multi-agent collaborative perception could significantly upgrade the perception performance by enabling agents to share complementary information with each other through communicat…
Collaborative Perception for Autonomous Driving: Current Status and Future Trend
Shunli Ren, Siheng Chen, Wenjun Zhang
Perception is one of the crucial module of the autonomous driving system, which has made great progress recently. However, limited ability of individual vehicles results in the bot…
Neural Message Passing for Visual Relationship Detection
Yue Hu, Siheng Chen, Xu Chen +2
Visual relationship detection aims to detect the interactions between objects in an image; however, this task suffers from combinatorial explosion due to the variety of objects and…
Weakly Supervised Online Action Detection for Infant General Movements
Tongyi Luo, Jia Xiao, Chuncao Zhang +5
To make the earlier medical intervention of infants' cerebral palsy (CP), early diagnosis of brain damage is critical. Although general movements assessment(GMA) has shown promisin…
Aerial Monocular 3D Object Detection
Yue Hu, Shaoheng Fang, Weidi Xie +1
Drones equipped with cameras can significantly enhance human ability to perceive the world because of their remarkable maneuverability in 3D space. Ironically, object detection for…
Skeleton-Parted Graph Scattering Networks for 3D Human Motion Prediction
Maosen Li, Siheng Chen, Zijing Zhang +3
Graph convolutional network based methods that model the body-joints' relations, have recently shown great promise in 3D skeleton-based human motion prediction. However, these meth…
Latency-Aware Collaborative Perception
Zixing Lei, Shunli Ren, Yue Hu +2
Collaborative perception has recently shown great potential to improve perception capabilities over single-agent perception. Existing collaborative perception methods usually consi…
Aware of the History: Trajectory Forecasting with the Local Behavior Data
Yiqi Zhong, Zhenyang Ni, Siheng Chen +1
The historical trajectories previously passing through a location may help infer the future trajectory of an agent currently at this location. Despite great improvements in traject…
Collaborative Uncertainty Benefits Multi-Agent Multi-Modal Trajectory Forecasting
Bohan Tang, Yiqi Zhong, Chenxin Xu +5
In multi-modal multi-agent trajectory forecasting, two major challenges have not been fully tackled: 1) how to measure the uncertainty brought by the interaction module that causes…
Dynamic-Group-Aware Networks for Multi-Agent Trajectory Prediction with Relational Reasoning
Chenxin Xu, Yuxi Wei, Bohan Tang +3
Demystifying the interactions among multiple agents from their past trajectories is fundamental to precise and interpretable trajectory prediction. However, previous works mainly c…
Hierarchical Spherical CNNs with Lifting-based Adaptive Wavelets for Pooling and Unpooling
Mingxing Xu, Chenglin Li, Wenrui Dai +4
Pooling and unpooling are two essential operations in constructing hierarchical spherical convolutional neural networks (HS-CNNs) for comprehensive feature learning in the spherica…
GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational Reasoning
Chenxin Xu, Maosen Li, Zhenyang Ni +2
Demystifying the interactions among multiple agents from their past trajectories is fundamental to precise and interpretable trajectory prediction. However, previous works only con…