Stars
Learning A Spiking Neural Network for Efficient Image Deraining (IJCAI 2024)
[CVPR 2022--Oral] Restormer: Efficient Transformer for High-Resolution Image Restoration. SOTA for motion deblurring, image deraining, denoising (Gaussian/real data), and defocus deblurring.
The state-of-the-art image restoration model without nonlinear activation functions.
Paper list for SNN based computer vision tasks.
🔥 This repository compiles research papers (from top-tier conferences and journals) and code implementations in the field of Spiking Neural Networks (SNNs). 本仓库收集了脉冲神经网络领域的顶会顶刊论文和代码，正在持续更新中。
Codes of the paper: SpikingResformer: Bridging ResNet and Vision Transformer in Spiking Neural Networks (CVPR2024)
This repo is for source code of paper "SpikeGraphormer: A High-Performance Graph Transformer with Spiking Graph Attention".
Offical implementation of "Spike-driven Transformer" (NeurIPS2023)
Create a new backward path for more accurate SNN gradients.
Awesome Spiking Neural Networks
Offical implementation of "Gated Attention Coding for Training High-performance and Efficient Spiking Neural Networks" (AAAI2024)
Ternary Spike: Learning Ternary Spikes for Spiking Neural Networks
Offical Implementation of "CLIF: Complementary Leaky Integrate-and-Fire Neuron for Spiking Neural Networks" (ICML 2024 spotlight)
[TNNLS 2024] Implementation of "TCJA-SNN: Temporal-Channel Joint Attention for Spiking Neural Networks"
[WACV 2024] Spiking Denoising Diffusion Probabilistic Models
GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networks, NeurIPS 2022 Poster
Spiking-DDPG trains an SNN for energy-efficient mapless navigation on Intel's Loihi neuromorphic processor.
[ECCV2022] [T-PAMI] StARformer: Transformer with State-Action-Reward Representations.
Stereo depth estimation with Spiking Neural Networks (SNN) on Dynamic Vision Sensor (DVS) data
Event-based Vision Resources. Community effort to collect knowledge on event-based vision technology (papers, workshops, datasets, code, videos, etc)
Optical Flow estimation from Event cameras and Spiking Neural Networks
Self-Supervised Learning of Event-based Optical Flow with Spiking Neural Networks