262 citations · 1.9k across the 56 of their papers we have counts for
7 papers · 1 filter
Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling
Xumin Yu, Lulu Tang, Yongming Rao +3
We present Point-BERT, a new paradigm for learning Transformers to generalize the concept of BERT to 3D point cloud. Inspired by BERT, we devise a Masked Point Modeling (MPM) task…
Optical Flow Estimation for Spiking Camera
Liwen Hu, Rui Zhao, Ziluo Ding +4
As a bio-inspired sensor with high temporal resolution, the spiking camera has an enormous potential in real applications, especially for motion estimation in high-speed scenes. Ho…
Spatio-Temporal Recurrent Networks for Event-Based Optical Flow Estimation
Ziluo Ding, Rui Zhao, Jiyuan Zhang +4
Event camera has offered promising alternative for visual perception, especially in high speed and high dynamic range scenes. Recently, many deep learning methods have shown great…
Pruning of Deep Spiking Neural Networks through Gradient Rewiring
Yanqi Chen, Zhaofei Yu, Wei Fang +2
Spiking Neural Networks (SNNs) have been attached great importance due to their biological plausibility and high energy-efficiency on neuromorphic chips. As these chips are usually…
Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networks
Jianhao Ding, Zhaofei Yu, Yonghong Tian +1
Spiking Neural Networks (SNNs), as bio-inspired energy-efficient neural networks, have attracted great attentions from researchers and industry. The most efficient way to train dee…
Spike Camera and Its Coding Methods
Siwei Dong, Tiejun Huang, Yonghong Tian
This paper introduces a spike camera with a distinct video capture scheme and proposes two methods of decoding the spike stream for texture reconstruction. The spike camera capture…
Deep Residual Learning in Spiking Neural Networks
Wei Fang, Zhaofei Yu, Yanqi Chen +3
Deep Spiking Neural Networks (SNNs) present optimization difficulties for gradient-based approaches due to discrete binary activation and complex spatial-temporal dynamics. Conside…