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SMTrack: End-to-End Trained Spiking Neural Networks for Multi-Object Tracking in RGB Videos
Pengzhi Zhong, Xinzhe Wang, Dan Zeng +3
Brain-inspired Spiking Neural Networks (SNNs) exhibit significant potential for low-power computation, yet their application in visual tasks remains largely confined to image class…
Learning Occlusion-Robust Vision Transformers for Real-Time UAV Tracking
You Wu, Xucheng Wang, Xiangyang Yang +4
Single-stream architectures using Vision Transformer (ViT) backbones show great potential for real-time UAV tracking recently. However, frequent occlusions from obstacles like buil…
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…