no code implementations • 15 Apr 2024 • Zhaokun Zhou, Qiulin Wang, Bin Lin, Yiwei Su, Rui Chen, Xin Tao, Amin Zheng, Li Yuan, Pengfei Wan, Di Zhang
To further evaluate the IAA capability of MLLMs, we construct the UNIAA-Bench, which consists of three aesthetic levels: Perception, Description, and Assessment.
2 code implementations • 25 Mar 2024 • Chenlin Zhou, Han Zhang, Zhaokun Zhou, Liutao Yu, Liwei Huang, Xiaopeng Fan, Li Yuan, Zhengyu Ma, Huihui Zhou, Yonghong Tian
ii) We incorporate the hierarchical structure, which significantly benefits the performance of both the brain and artificial neural networks, into spiking transformers to obtain multi-scale spiking representation.
1 code implementation • 15 Feb 2024 • Man Yao, Jiakui Hu, Tianxiang Hu, Yifan Xu, Zhaokun Zhou, Yonghong Tian, Bo Xu, Guoqi Li
CNN-based SNNs are the current mainstream of neuromorphic computing.
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 • 14 Dec 2023 • Yuqing Wang, Zhenyu Weng, Zhaokun Zhou, Shuaijian Ji, Zhongjie Ye, Yuesheng Zhu
Over the past years, Printed Mathematical Expression Recognition (PMER) has progressed rapidly.
1 code implementation • NeurIPS 2023 • Man Yao, Jiakui Hu, Zhaokun Zhou, Li Yuan, Yonghong Tian, Bo Xu, Guoqi Li
In this paper, we incorporate the spike-driven paradigm into Transformer by the proposed Spike-driven Transformer with four unique properties: 1) Event-driven, no calculation is triggered when the input of Transformer is zero; 2) Binary spike communication, all matrix multiplications associated with the spike matrix can be transformed into sparse additions; 3) Self-attention with linear complexity at both token and channel dimensions; 4) The operations between spike-form Query, Key, and Value are mask and addition.
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.
1 code implementation • 10 May 2023 • Chenlin Zhou, Han Zhang, Zhaokun Zhou, Liutao Yu, Zhengyu Ma, Huihui Zhou, Xiaopeng Fan, Yonghong Tian
In this paper, we propose ConvBN-MaxPooling-LIF (CML), an SNN-optimized downsampling with precise gradient backpropagation.
1 code implementation • NeurIPS 2023 • Wei Fang, Zhaofei Yu, Zhaokun Zhou, Ding Chen, Yanqi Chen, Zhengyu Ma, Timothée Masquelier, Yonghong Tian
Vanilla spiking neurons in Spiking Neural Networks (SNNs) use charge-fire-reset neuronal dynamics, which can only be simulated serially and can hardly learn long-time dependencies.
1 code implementation • 24 Apr 2023 • Chenlin Zhou, Liutao Yu, Zhaokun Zhou, Zhengyu Ma, Han Zhang, Huihui Zhou, Yonghong Tian
Based on this residual design, we develop Spikingformer, a pure transformer-based spiking neural network.
2 code implementations • 29 Sep 2022 • Zhaokun Zhou, Yuesheng Zhu, Chao He, YaoWei Wang, Shuicheng Yan, Yonghong Tian, Li Yuan
Spikformer (66. 3M parameters) with comparable size to SEW-ResNet-152 (60. 2M, 69. 26%) can achieve 74. 81% top1 accuracy on ImageNet using 4 time steps, which is the state-of-the-art in directly trained SNNs models.