30 citations · 59 across the 5 of their papers we have counts for
7 papers
Hyper-RED: Scalable Event Pre-training via Semantic Hypergraph Distillation
Meisen Wang, Zhiqiang Tian, Wei Bao +3
Event cameras have shown great potential for robust visual perception, yet scaling event representation learning remains challenging due to the scarcity of large-scale annotated ev…
ERetinex: Event Camera Meets Retinex Theory for Low-Light Image Enhancement
Xuejian Guo, Zhiqiang Tian, Yuehang Wang +4
Low-light image enhancement aims to restore the under-exposure image captured in dark scenarios. Under such scenarios, traditional frame-based cameras may fail to capture the struc…
Beyond Graphs: Can Large Language Models Comprehend Hypergraphs?
Yifan Feng, Chengwu Yang, Xingliang Hou +4
Existing benchmarks like NLGraph and GraphQA evaluate LLMs on graphs by focusing mainly on pairwise relationships, overlooking the high-order correlations found in real-world data.…
Hyper-YOLO: When Visual Object Detection Meets Hypergraph Computation
Yifan Feng, Jiangang Huang, Shaoyi Du +6
We introduce Hyper-YOLO, a new object detection method that integrates hypergraph computations to capture the complex high-order correlations among visual features. Traditional YOL…
Hypergraph-based Multi-View Action Recognition using Event Cameras
Yue Gao, Jiaxuan Lu, Siqi Li +2
Action recognition from video data forms a cornerstone with wide-ranging applications. Single-view action recognition faces limitations due to its reliance on a single viewpoint. I…
IA-LSTM: Interaction-Aware LSTM for Pedestrian Trajectory Prediction
Yuehai Chen
Predicting the trajectory of pedestrians in crowd scenarios is indispensable in self-driving or autonomous mobile robot field because estimating the future locations of pedestrians…
Learning Discriminative Features for Crowd Counting
Yuehai Chen, Qingzhong Wang, Jing Yang +3
Crowd counting models in highly congested areas confront two main challenges: weak localization ability and difficulty in differentiating between foreground and background, leading…