REACT is a fully spiking state‑space model that processes raw event‑camera data one event at a time, avoiding temporal accumulation and its associated delay. It employs a complex‑valued spiking neuron (C‑SiLIF) whose dynamics are driven by the inter‑event interval, enabling continuous‑time state updates at microsecond resolution. Evaluated on gesture recognition and time‑to‑collision estimation, REACT achieves low latency (4.6 ms) and high accuracy, supports anytime prediction, zero‑shot transfer, and INT8 quantization, dramatically reducing energy consumption.
By Geoffroy Keime, Nicolas Cuperlier, Benoit R. Cottereau
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.
By Pengzhi Zhong, Jiwei Mo, Dan Zeng, Feixiang He, Shuiwang Li
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...
By Christoffer Koo {\O}hrstr{\o}m, Ronja G\"uldenring, Lazaros Nalpantidis
SpikeMoE introduces a spike-based k‑WTA router that uses lateral inhibition and refractory periods to select the top‑K experts based on discrete spike counts, inspired by hippocampal CA1 competition. The framework combines spiking neural network dynamics with mixture‑of‑experts conditional computation and adds a two‑stage missing‑modality module for robust multimodal processing. Experiments on vision, language, and multimodal tasks show that SpikeMoE matches or surpasses ANN baselines while offering energy‑efficient performance.
By Xiaoli Liu, Yujie Liang, Jialin Li, Malu Zhang
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.
By Pengzhi Zhong, Jiwei Mo, Haolun Li, Ge Zheng, Jingqi Wang, Xinyi Bo, Shuiwang Li
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...
By Shuai Wang, Malu Zhang, Mingquan Liu, Weihui Dai, Dehao Zhang, Jieyuan Zhang, Yimeng Shan, Zijian Zhou, Yang Yang
arXiv:2608. 07712v1 Announce Type: cross Abstract: A predictive model receives a self-supervised signal whenever the consequence of an action is observed.
By Ziqiao Yu
arXiv:2607. 11914v1 Announce Type: cross Abstract: A central goal of current Spiking Neural Network (SNN) research is to improve their accuracy toward becoming low-power alternatives to Artificial Neural Networks (ANNs).
By Jiahong Zhang, Sijun Shen, Man Yao, Han Xu, Mingqiang Huang, Yonghong Tian, Bo Xu, Guoqi Li
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.
By Minsuk Jang, Yujin Yang, Hee-Seon Kim, Minseok Son, Younghun Kim, Changick Kim
arXiv:2607. 14672v1 Announce Type: new Abstract: Continuous-time spiking neural networks (SNNs) provide an event-driven framework for temporal computation, computational neuroscience, and neuromorphic hardware.
By Yusuke Sakemi, Tomoya Takeuchi, Takeo Hosomi, Kazuyuki Aihara
Mask IPL introduces a Computation Graph Clipping technique that removes noise from Intrinsic Position Learning (IPL) in spiking neural networks for event-based tracking. By applying a validity mask to each layer, the method aligns forward and backward propagation with ideal gradients without adding parameters. Experiments show that Mask IPL consistently improves tracking performance on Tiny‑scale and Base‑scale benchmarks such as FE108, FELT, and VisEvent.
By Yimeng Shan, Malu Zhang
arXiv:2609.37047v1 Announce Type: cross
Abstract: We propose a new spiking neural network (SNN) design to process static images and event streams using time-to-first-spike (TTFS) latencies. Our key r...
By Aidin Attar, Eleonora Cicciarella, Michele Rossi