6 papers
DeCo: Efficient Decouple-to-Couple Learning for Multi-Task Visual Grounding
Xiaoqiang Lu, Licheng Jiao, Long Sun +5
Multi-task visual grounding requires models to jointly understand linguistic semantics and perform accurate visual localization and segmentation. Despite the success of multimodal…
Solution for UCF UrbanTwin V2X-Real Track: Sim-to-Real Urban LiDAR 3D Object Detection
Pu Luo, Cong Xu, Yumei Li +4
Bridging the simulation-to-reality gap in roadside LiDAR requires addressing several coupled discrepancies, including scene geometry, sampling density, return patterns, and pedestr…
Solution for UCF UrbanTwin LUMPI Track: Sim-to-Real Urban LiDAR 3D Object Detection
Pu Luo, Cong Xu, Yumei Li +4
We present our solution to the LUMPI track of the UCF UrbanTwin Sim2Real LiDAR Challenge at the 6th DriveX Workshop, ECCV 2026. The detector must be trained only on synthetic data…
A Diff-Attention Aware State Space Fusion Model for Remote Sensing Classification
Wenping Ma, Boyou Xue, Mengru Ma +3
Multispectral (MS) and panchromatic (PAN) images describe the same land surface, so these images not only have their own advantages, but also have a lot of similar information. In…
Triple Point Masking
Jiaming Liu, Linghe Kong, Yue Wu +5
Existing 3D mask learning methods encounter performance bottlenecks under limited data, and our objective is to overcome this limitation. In this paper, we introduce a triple point…
M3SOT: Multi-frame, Multi-field, Multi-space 3D Single Object Tracking
Jiaming Liu, Yue Wu, Maoguo Gong +3
3D Single Object Tracking (SOT) stands a forefront task of computer vision, proving essential for applications like autonomous driving. Sparse and occluded data in scene point clou…