Efficient video human pose estimation must balance localisation accuracy, motion quality, and energy cost. We present SpikePose, a lightweight video pose estimator that pairs a frame-reset, single-ste...
arXiv:2510.26614v2 Announce Type: replace Abstract: We propose tokenization of events and present a tokenizer, Spiking Patches, specifically designed for event cameras. Given a stream of asynchronous...
arXiv:2609.24403v1 Announce Type: new Abstract: Biological visual systems achieve continuous, low-latency motion perception by processing sparse, asynchronous spiking signals, enabling real-time trac...
SBMVTrack is a fully spiking neural network framework designed for energy-efficient UAV visual tracking. It introduces Energy-Weighted Spike Budgeting (EWSB) to constrain spike activity based on computational cost, and Masked Multi-View Target Modeling (MVTM) to enhance target representation by leveraging correlated temporal views. Experiments on multiple benchmarks show that SBMVTrack reduces spike firing rates and theoretical energy consumption while maintaining competitive tracking accuracy.
arXiv:2610.09710v1 Announce Type: new Abstract: ANN-to-SNN conversion offers a practical route toward energy-efficient spiking Vision-Language-Action (VLA) models by bypassing the substantial cost of...
arXiv:2609.39514v1 Announce Type: new Abstract: Vision-language-action (VLA) models bridge multimodal understanding and robotic control, advancing the dominant paradigm for embodied intelligence. How...
The paper evaluates a spiking version of the MoveNet architecture, called S-MoveNet-Lightning, for autonomous spacecraft pose estimation. Using the RAPTOR dataset, it compares the spiking model to the standard MoveNet-Lightning, finding that the spiking version requires on average 2.2 times fewer multiplication and accumulation operations on FPGA platforms with AMD Deep Learning Processor Units. The study highlights balanced advantages in computational resources and architectural settings for spiking U‑Net‑like architectures in embedded, event‑driven space applications.
arXiv:2610.17154v2 Announce Type: replace-cross Abstract: Decoding visual stimuli from the spiking activity of retinal ganglion cells (RGCs) informs both our understanding of the retinal code and the...
arXiv:2604. 08894v2 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs) offer superior energy efficiency over Artificial Neural Networks (ANNs).
arXiv:2609.22500v1 Announce Type: new Abstract: Autonomous navigation requires precise and efficient semantic segmentation, yet existing frame-based approaches remain limited by motion blur, glare, l...
STATrack is a fully spiking neural network designed for UAV visual tracking using only RGB inputs, eliminating the need for costly event cameras. It introduces Adaptive Mutual Information Maximization (AMIM) to preserve fine-grained target information in deep spiking representations and a sample-difficulty-aware dynamic weighting strategy to adjust the mutual‑information constraint during training. Experiments on four UAV tracking benchmarks show that STATrack achieves state‑of‑the‑art performance while maintaining low theoretical energy consumption.
arXiv:2605. 05895v2 Announce Type: replace-cross Abstract: Modern AI-generated videos are photorealistic at the single-frame level, leaving inter-frame dynamics as the main remaining axis for detection.