1 citations · 2 across the 7 of their papers we have counts for
7 papers
TUMTraf EMOT: Event-Based Multi-Object Tracking Dataset and Baseline for Traffic Scenarios
Mengyu Li, Xingcheng Zhou, Guang Chen +2
In Intelligent Transportation Systems (ITS), multi-object tracking is primarily based on frame-based cameras. However, these cameras tend to perform poorly under dim lighting and h…
HGL: Hierarchical Geometry Learning for Test-time Adaptation in 3D Point Cloud Segmentation
Tianpei Zou, Sanqing Qu, Zhijun Li +4
3D point cloud segmentation has received significant interest for its growing applications. However, the generalization ability of models suffers in dynamic scenarios due to the di…
Embracing Events and Frames with Hierarchical Feature Refinement Network for Object Detection
Hu Cao, Zehua Zhang, Yan Xia +4
In frame-based vision, object detection faces substantial performance degradation under challenging conditions due to the limited sensing capability of conventional cameras. Event…
MAP: MAsk-Pruning for Source-Free Model Intellectual Property Protection
Boyang Peng, Sanqing Qu, Yong Wu +5
Deep learning has achieved remarkable progress in various applications, heightening the importance of safeguarding the intellectual property (IP) of well-trained models. It entails…
LEAD: Learning Decomposition for Source-free Universal Domain Adaptation
Sanqing Qu, Tianpei Zou, Lianghua He +4
Universal Domain Adaptation (UniDA) targets knowledge transfer in the presence of both covariate and label shifts. Recently, Source-free Universal Domain Adaptation (SF-UniDA) has…
PCDepth: Pattern-based Complementary Learning for Monocular Depth Estimation by Best of Both Worlds
Haotian Liu, Sanqing Qu, Fan Lu +4
Event cameras can record scene dynamics with high temporal resolution, providing rich scene details for monocular depth estimation (MDE) even at low-level illumination. Therefore,…
GarchingSim: An Autonomous Driving Simulator with Photorealistic Scenes and Minimalist Workflow
Liguo Zhou, Yinglei Song, Yichao Gao +10
Conducting real road testing for autonomous driving algorithms can be expensive and sometimes impractical, particularly for small startups and research institutes. Thus, simulation…