1 code implementation • 4 Jan 2024 • Zhaokun Zhou, Kaiwei Che, Wei Fang, Keyu Tian, Yuesheng Zhu, Shuicheng Yan, Yonghong Tian, Li Yuan
To the best of our knowledge, this is the first time that the SNN achieves 80+% accuracy on ImageNet.
no code implementations • 28 Aug 2023 • Yemin li, Zhongcheng Liu, Xiaoying Lou, Mirigual Kurban, Miao Li, Jie Yang, Kaiwei Che, Jiankun Wang, Max Q. -H Meng, Yan Huang, Qin Guo, Pinjin Hu
A total of 5105 images of 154 intestinal segments from 87 patients undergoing EC treatment at a center in China between March 2022 and March 2023 are scored according to the Geboes score.
no code implementations • 24 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.
no code implementations • 1 Jun 2023 • Kaiwei Che, Zhaokun Zhou, Zhengyu Ma, Wei Fang, Yanqi Chen, Shuaijie Shen, Li Yuan, Yonghong Tian
The integration of self-attention mechanisms into Spiking Neural Networks (SNNs) has garnered considerable interest in the realm of advanced deep learning, primarily due to their biological properties.
no code implementations • 24 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.
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.
no code implementations • 6 Aug 2021 • Kaiwei Che, Chengwei Ye, Yibing Yao, Nachuan Ma, Ruo Zhang, Jiankun Wang, Max Q. -H. Meng
Second, a ResNet-101 based network is used to detect three biological anatomical landmarks separately to obtain the intermediate detection results.