1 citations · 4 across the 13 of their papers we have counts for
14 papers
SBMVTrack: Spike-Budgeted Multi-View Learning for Power-Efficient UAV Tracking
Pengzhi Zhong, Jiwei Mo, Haolun Li +4
With sparse and event-driven computation, spiking neural networks show great potential for achieving accurate and power-efficient UAV visual tracking. However, existing SNN-based t…
SpikingMOT: A Spike-Driven Multi-Object Tracker
Yiding Sun, Xiangyang Yang, Dongxu Zhang +7
Multi-object tracking (MOT) plays a fundamental role in visual perception, where accurate trajectory prediction is essential for reliable target association under complex motion pa…
STATrack: A Target-Aware Fully Spiking Neural Network for 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…
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…
Low-Light Object Tracking: A Benchmark
Pengzhi Zhong, Xiaoyu Guo, Defeng Huang +4
In recent years, the field of visual tracking has made significant progress with the application of large-scale training datasets. These datasets have supported the development of…
Camouflaged Object Tracking: A Benchmark
Xiaoyu Guo, Pengzhi Zhong, Hao Zhang +4
Visual tracking has seen remarkable advancements, largely driven by the availability of large-scale training datasets that have enabled the development of highly accurate and robus…
Tracking Reflected Objects: A Benchmark
Xiaoyu Guo, Pengzhi Zhong, Lizhi Lin +3
Visual tracking has advanced significantly in recent years, mainly due to the availability of large-scale training datasets. These datasets have enabled the development of numerous…
Towards Reflected Object Detection: A Benchmark
Yiquan Wu, Zhongtian Wang, You Wu +3
Object detection has greatly improved over the past decade thanks to advances in deep learning and large-scale datasets. However, detecting objects reflected in surfaces remains an…
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…
Tracking Transforming Objects: A Benchmark
You Wu, Yuelong Wang, Yaxin Liao +3
Tracking transforming objects holds significant importance in various fields due to the dynamic nature of many real-world scenarios. By enabling systems accurately represent transf…