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20212023
most citedDeep Directly-Trained Spiking Neural Networks for Object Detection

15 citations · 32 across the 11 of their papers we have counts for

collaborators

11 papers

cs.NE2023★ 2 cited

Inherent Redundancy in Spiking Neural Networks

Man Yao, Jiakui Hu, Guangshe Zhao +4

Spiking Neural Networks (SNNs) are well known as a promising energy-efficient alternative to conventional artificial neural networks. Subject to the preconceived impression that SN…

cs.CV2023★ 15 cited

Deep Directly-Trained Spiking Neural Networks for Object Detection

Qiaoyi Su, Yuhong Chou, Yifan Hu +4

Spiking neural networks (SNNs) are brain-inspired energy-efficient models that encode information in spatiotemporal dynamics. Recently, deep SNNs trained directly have shown great…

cs.CV2023★ 2 cited

Dual Memory Aggregation Network for Event-Based Object Detection with Learnable Representation

Dongsheng Wang, Xu Jia, Yang Zhang +5

Event-based cameras are bio-inspired sensors that capture brightness change of every pixel in an asynchronous manner. Compared with frame-based sensors, event cameras have microsec…

cs.LG2022★ 1 cited

Statistical Physics of Deep Neural Networks: Initialization toward Optimal Channels

Kangyu Weng, Aohua Cheng, Ziyang Zhang +2

In deep learning, neural networks serve as noisy channels between input data and its representation. This perspective naturally relates deep learning with the pursuit of constructi…

cs.CV2022★ 2 cited

MSS-DepthNet: Depth Prediction with Multi-Step Spiking Neural Network

Xiaoshan Wu, Weihua He, Man Yao +3

Event cameras are considered to have great potential for computer vision and robotics applications because of their high temporal resolution and low power consumption characteristi…

cs.CV2022★ 3 cited

Continuously Controllable Facial Expression Editing in Talking Face Videos

Zhiyao Sun, Yu-Hui Wen, Tian Lv +4

Recently audio-driven talking face video generation has attracted considerable attention. However, very few researches address the issue of emotional editing of these talking face…