Search Results for author: Luziwei Leng

Found 9 papers, 1 papers with code

Automotive Object Detection via Learning Sparse Events by Spiking Neurons

no code implementations24 Jul 2023 Hu Zhang, Yanchen Li, Luziwei Leng, Kaiwei Che, Qian Liu, Qinghai Guo, Jianxing Liao, Ran Cheng

Traditional object detection techniques that utilize Artificial Neural Networks (ANNs) face challenges due to the sparse and asynchronous nature of the events these sensors capture.

Event-based vision object-detection +1

Efficient Deep Spiking Multi-Layer Perceptrons with Multiplication-Free Inference

no code implementations21 Jun 2023 Boyan Li, Luziwei Leng, Ran Cheng, Shuaijie Shen, Kaixuan Zhang, JianGuo Zhang, Jianxing Liao

An expanded version of our network challenges the performance of the spiking VGG-16 network with a 71. 64% top-1 accuracy, all while operating with a model capacity 2. 1 times smaller.

Image Classification

Accurate and Efficient Event-based Semantic Segmentation Using Adaptive Spiking Encoder-Decoder Network

no code implementations24 Apr 2023 Rui Zhang, Luziwei Leng, Kaiwei Che, Hu Zhang, Jie Cheng, Qinghai Guo, Jiangxing Liao, Ran Cheng

Leveraging the low-power, event-driven computation and the inherent temporal dynamics, spiking neural networks (SNNs) are potentially ideal solutions for processing dynamic and asynchronous signals from event-based sensors.

Event-based vision Semantic Segmentation

Neuro-Modulated Hebbian Learning for Fully Test-Time Adaptation

no code implementations CVPR 2023 Yushun Tang, Ce Zhang, Heng Xu, Shuoshuo Chen, Jie Cheng, Luziwei Leng, Qinghai Guo, Zhihai He

We observe that the performance of this feed-forward Hebbian learning for fully test-time adaptation can be significantly improved by incorporating a feedback neuro-modulation layer.

Test-time Adaptation

Discrete Time Convolution for Fast Event-Based Stereo

1 code implementation CVPR 2022 Kaixuan Zhang, Kaiwei Che, JianGuo Zhang, Jie Cheng, Ziyang Zhang, Qinghai Guo, Luziwei Leng

Inspired by continuous dynamics of biological neuron models, we propose a novel encoding method for sparse events - continuous time convolution (CTC) - which learns to model the spatial feature of the data with intrinsic dynamics.

Depth Estimation Stereo Matching

Spiking neurons with short-term synaptic plasticity form superior generative networks

no code implementations24 Sep 2017 Luziwei Leng, Roman Martel, Oliver Breitwieser, Ilja Bytschok, Walter Senn, Johannes Schemmel, Karlheinz Meier, Mihai A. Petrovici

In this work, we use networks of leaky integrate-and-fire neurons that are trained to perform both discriminative and generative tasks in their forward and backward information processing paths, respectively.

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