A Novel Spike Transformer Network for Depth Estimation from Event Cameras via Cross-modality Knowledge Distillation

26 April 2024

Papers citing "A Novel Spike Transformer Network for Depth Estimation from Event Cameras via Cross-modality Knowledge Distillation"

9 / 9 papers shown

Title
SDformerFlow: Spatiotemporal swin spikeformer for event-based optical flow estimation Yi Tian Juan Andrade-Cetto 29 0 0 06 Sep 2024
Spikformer: When Spiking Neural Network Meets Transformer Zhaokun Zhou Yuesheng Zhu Chao He Yaowei Wang Shuicheng Yan Yonghong Tian Liuliang Yuan 145 241 0 29 Sep 2022
Real-Time Grasping Strategies Using Event Camera Xiaoqian Huang Mohamad Halwani Rajkumar Muthusamy Abdulla Ayyad Dewald Swart Lakmal Seneviratne D. Gan Yahya Zweiri 37 30 0 15 Jul 2021
Learning from Event Cameras with Sparse Spiking Convolutional Neural Networks Loic Cordone Benoit Miramond Sonia Ferrante 33 36 0 26 Apr 2021
Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions Wenhai Wang Enze Xie Xiang Li Deng-Ping Fan Kaitao Song Ding Liang Tong Lu Ping Luo Ling Shao ViT 277 3,622 0 24 Feb 2021
Deep Residual Learning in Spiking Neural Networks Wei Fang Zhaofei Yu Yanqing Chen Tiejun Huang T. Masquelier Yonghong Tian 123 476 0 08 Feb 2021
Learning Monocular Dense Depth from Events Javier Hidalgo-Carrió Daniel Gehrig Davide Scaramuzza MDE 43 113 0 16 Oct 2020
Spike-FlowNet: Event-based Optical Flow Estimation with Energy-Efficient Hybrid Neural Networks Chankyu Lee Adarsh Kosta A. Z. Zhu Kenneth Chaney Kostas Daniilidis Kaushik Roy 88 160 0 14 Mar 2020
Monocular Depth Estimation Based On Deep Learning: An Overview Chaoqiang Zhao Qiyu Sun Chongzhen Zhang Yang Tang Feng Qian MDE 60 251 0 14 Mar 2020