1 citations · 3 across the 8 of their papers we have counts for
5 papers · 2 filters
Learning an Adaptive and View-Invariant Vision Transformer for Real-Time UAV Tracking
You Wu, Yongxin Li, Mengyuan Liu +6
Transformer-based models have improved visual tracking, but most still cannot run in real time on resource-limited devices, especially for unmanned aerial vehicle (UAV) tracking. T…
MambaNUT: Nighttime UAV Tracking via Mamba-based Adaptive Curriculum Learning
You Wu, Xiangyang Yang, Xucheng Wang +3
Harnessing low-light enhancement and domain adaptation, nighttime UAV tracking has made substantial strides. However, over-reliance on image enhancement, limited high-quality night…
GenUDC: High Quality 3D Mesh Generation with Unsigned Dual Contouring Representation
Ruowei Wang, Jiaqi Li, Dan Zeng +4
Generating high-quality meshes with complex structures and realistic surfaces is the primary goal of 3D generative models. Existing methods typically employ sequence data or deform…
Learning Motion Blur Robust Vision Transformers for Real-Time UAV Tracking
You Wu, Xucheng Wang, Dan Zeng +4
Unmanned aerial vehicle (UAV) tracking is critical for applications like surveillance, search-and-rescue, and autonomous navigation. However, the high-speed movement of UAVs and ta…
Adaptively Bypassing Vision Transformer Blocks for Efficient Visual Tracking
Xiangyang Yang, Dan Zeng, Xucheng Wang +4
Empowered by transformer-based models, visual tracking has advanced significantly. However, the slow speed of current trackers limits their applicability on devices with constraine…