1 code implementation • 11 Dec 2023 • Yufei Guo, Yuanpei Chen, Xiaode Liu, Weihang Peng, Yuhan Zhang, Xuhui Huang, Zhe Ma
To handle the problem, we propose a ternary spike neuron to transmit information.
1 code implementation • ICCV 2023 • Yufei Guo, Yuhan Zhang, Yuanpei Chen, Weihang Peng, Xiaode Liu, Liwen Zhang, Xuhui Huang, Zhe Ma
All these BNs are suggested to be used after the convolution layer as usually doing in CNNs.
2 code implementations • ICCV 2023 • Yufei Guo, Xiaode Liu, Yuanpei Chen, Liwen Zhang, Weihang Peng, Yuhan Zhang, Xuhui Huang, Zhe Ma
Spiking Neural Networks (SNNs) as one of the biology-inspired models have received much attention recently.
no code implementations • 10 Jul 2023 • Yufei Guo, Yuanpei Chen, Liwen Zhang, Xiaode Liu, Xinyi Tong, Yuanyuan Ou, Xuhui Huang, Zhe Ma
The Spiking Neural Network (SNN) has attracted more and more attention recently.
no code implementations • 31 May 2023 • Yufei Guo, Xuhui Huang, Zhe Ma
The spiking neural network (SNN), as a promising brain-inspired computational model with binary spike information transmission mechanism, rich spatially-temporal dynamics, and event-driven characteristics, has received extensive attention.
no code implementations • 3 May 2023 • Yufei Guo, Weihang Peng, Yuanpei Chen, Liwen Zhang, Xiaode Liu, Xuhui Huang, Zhe Ma
In this paper, we propose a joint training framework of ANN and SNN, in which the ANN can guide the SNN's optimization.
no code implementations • ICCV 2023 • Mengxue Kang, Jinpeng Zhang, Jinming Zhang, Xiashuang Wang, Yang Chen, Zhe Ma, Xuhui Huang
However, previous works on feature distillation heavily rely on low-level feature information, while under-exploring the importance of high-level semantic information.
2 code implementations • CVPR 2023 • Liwen Zhang, Xinyan Zhang, Youcheng Zhang, Yufei Guo, Yuanpei Chen, Xuhui Huang, Zhe Ma
However, neither the regular convolution operation nor the modified ones are specific to interpret radar signals.
1 code implementation • CVPR 2023 • Qi Ming, Lingjuan Miao, Zhe Ma, Lin Zhao, Zhiqiang Zhou, Xuhui Huang, Yuanpei Chen, Yufei Guo
In this paper, we propose a Gradient-Corrected IoU (GCIoU) loss to achieve fast and accurate 3D bounding box regression.
no code implementations • NeurIPS 2022 • Yufei Guo, Yuanpei Chen, Liwen Zhang, Xiaode Liu, YingLei Wang, Xuhui Huang, Zhe Ma
To deal with this problem, the Information maximization loss (IM-Loss) that aims at maximizing the information flow in the SNN is proposed in the paper.
Ranked #6 on Event data classification on CIFAR10-DVS
1 code implementation • 13 Oct 2022 • Yufei Guo, Liwen Zhang, Yuanpei Chen, Xinyi Tong, Xiaode Liu, YingLei Wang, Xuhui Huang, Zhe Ma
Motivated by this assumption, a training-inference decoupling method for SNNs named as Real Spike is proposed, which not only enjoys both unshared convolution kernels and binary spikes in inference-time but also maintains both shared convolution kernels and Real-valued Spikes during training.
no code implementations • CVPR 2022 • Yufei Guo, Xinyi Tong, Yuanpei Chen, Liwen Zhang, Xiaode Liu, Zhe Ma, Xuhui Huang
Unfortunately, with the propagation of binary spikes, the distribution of membrane potential will shift, leading to degeneration, saturation, and gradient mismatch problems, which would be disadvantageous to the network optimization and convergence.
1 code implementation • 14 Jul 2021 • Wenqi Zeng, Siqin Cao, Xuhui Huang, Yuan YAO
Therefore, to learn rare events of slow molecular dynamics by LSTM and Transformer, it is critical to choose proper temporal resolution (i. e., saving intervals of MD simulation trajectories) and state partition in high resolution data, since deep neural network models might not automatically disentangle slow dynamics from fast dynamics when both are present in data influencing each other.
no code implementations • CVPR 2021 • Chaofan Chen, Xiaoshan Yang, Changsheng Xu, Xuhui Huang, Zhe Ma
Specifically, we first employ the comparison module to explore the pairwise sample relations to learn rich sample representations in the instance-level graph.
1 code implementation • 17 Dec 2018 • Yunzhe Hao, Xuhui Huang, Meng Dong, Bo Xu
By combining the sym-STDP rule with bio-plausible synaptic scaling and intrinsic plasticity of the dynamic threshold, our SNN model implemented SL well and achieved good performance in the benchmark recognition task (MNIST dataset).
1 code implementation • 20 Oct 2018 • Yin Xian, Hanlin Gu, Wei Wang, Xuhui Huang, Yuan YAO, Yang Wang, Jian-Feng Cai
We introduce the use of data-driven tight frame (DDTF) algorithm for cryo-EM image denoising.
Computation Image and Video Processing