no code implementations • 9 May 2024 • Hongwei Ren, Yue Zhou, Jiadong Zhu, Haotian Fu, Yulong Huang, Xiaopeng Lin, Yuetong Fang, Fei Ma, Hao Yu, Bojun Cheng
However, this approach neglects the sparsity of event data, loses fine-grained temporal information during the transformation process, and increases the computational burden, making it ineffective for characterizing event camera properties.
1 code implementation • 17 Apr 2024 • Zuowen Wang, Chang Gao, Zongwei Wu, Marcos V. Conde, Radu Timofte, Shih-Chii Liu, Qinyu Chen, Zheng-Jun Zha, Wei Zhai, Han Han, Bohao Liao, Yuliang Wu, Zengyu Wan, Zhong Wang, Yang Cao, Ganchao Tan, Jinze Chen, Yan Ru Pei, Sasskia Brüers, Sébastien Crouzet, Douglas McLelland, Oliver Coenen, Baoheng Zhang, Yizhao Gao, Jingyuan Li, Hayden Kwok-Hay So, Philippe Bich, Chiara Boretti, Luciano Prono, Mircea Lică, David Dinucu-Jianu, Cătălin Grîu, Xiaopeng Lin, Hongwei Ren, Bojun Cheng, Xinan Zhang, Valentin Vial, Anthony Yezzi, James Tsai
This survey reviews the AIS 2024 Event-Based Eye Tracking (EET) Challenge.
no code implementations • 28 Mar 2024 • Hongwei Ren, Jiadong Zhu, Yue Zhou, Haotian Fu, Yulong Huang, Bojun Cheng
These cameras implicitly capture movement and depth information in events, making them appealing sensors for Camera Pose Relocalization (CPR) tasks.
no code implementations • 21 Feb 2024 • Haobo Liu, Zhengyang Qian, Wei Wu, Hongwei Ren, Zhiwei Liu, Leibin Ni
Moreover, a novel FP-DAC is also implemented which reconstructs FP digital codes into analog values to perform analog computation.
no code implementations • 7 Feb 2024 • Yulong Huang, Xiaopeng Lin, Hongwei Ren, Yue Zhou, Zunchang Liu, Haotian Fu, Biao Pan, Bojun Cheng
We link the degraded accuracy to the vanishing of gradient on the temporal dimension through the analytical and experimental study of the training process of Leaky Integrate-and-Fire (LIF) Neuron-based SNNs.
no code implementations • 11 Oct 2023 • Hongwei Ren, Yue Zhou, Yulong Huang, Haotian Fu, Xiaopeng Lin, Jie Song, Bojun Cheng
Moreover, it also achieves SOTA performance across all methods on three datasets, utilizing approximately 0. 3\% of the parameters and 0. 5\% of power consumption employed by artificial neural networks (ANNs).
no code implementations • 19 Aug 2023 • Hongwei Ren, Yue Zhou, Haotian Fu, Yulong Huang, Renjing Xu, Bojun Cheng
In the experiment, TTPOINT emerged as the SOTA method on three datasets while also attaining SOTA among point cloud methods on all five datasets.
no code implementations • 11 Apr 2023 • Hongwei Ren, Yuhong Shi, Kewei Liang
The most commonly used models for this task are autoregressive models, such as recurrent neural networks (RNNs) or variants, and Transformer Networks.