Showing 2026Show all
3 papers · 1 filter
cs.CV2026
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…
cs.CV2026
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…
cs.CV2026
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…