6 papers
Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking
Pengzhi Zhong, Jiwei Mo, Dan Zeng +2
Spiking Neural Networks (SNNs), characterized by their event-driven computation and low power consumption, have shown great potential for energy-efficient visual tracking on unmann…
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…
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 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…
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…