no code implementations • 22 Oct 2023 • Yingkai Fu, Meng Li, Wenxi Liu, Yuanchen Wang, Jiqing Zhang, BaoCai Yin, Xiaopeng Wei, Xin Yang
We demonstrate that our tracker has superior performance against the state-of-the-art trackers in terms of both accuracy and efficiency.
1 code implementation • 6 Oct 2023 • Haiwei Zhang, Jiqing Zhang, Bo Dong, Pieter Peers, Wenwei Wu, Xiaopeng Wei, Felix Heide, Xin Yang
To the best of our knowledge, our method is the first eye-based emotion recognition method that leverages event-based cameras and spiking neural network.
no code implementations • CVPR 2023 • Jiqing Zhang, Yuanchen Wang, Wenxi Liu, Meng Li, Jinpeng Bai, BaoCai Yin, Xin Yang
The alignment module is responsible for cross-style and cross-frame-rate alignment between frame and event modalities under the guidance of the moving cues furnished by events.
no code implementations • CVPR 2022 • Jiqing Zhang, Bo Dong, Haiwei Zhang, Jianchuan Ding, Felix Heide, BaoCai Yin, Xin Yang
In particular, the proposed architecture features a transformer module to provide global spatial information and a spiking neural network (SNN) module for extracting temporal cues.
2 code implementations • ICCV 2021 • Jiqing Zhang, Xin Yang, Yingkai Fu, Xiaopeng Wei, BaoCai Yin, Bo Dong
Our approach's effectiveness is enforced by a novel designed cross-domain attention schemes, which can effectively enhance features based on self- and cross-domain attention schemes; The adaptiveness is guarded by a specially designed weighting scheme, which can adaptively balance the contribution of the two domains.
Ranked #3 on Object Tracking on FE108
no code implementations • 10 Aug 2021 • Jiqing Zhang, Kai Zhao, Bo Dong, Yingkai Fu, Yuxin Wang, Xin Yang, BaoCai Yin
Jointly exploiting multiple different yet complementary domain information has been proven to be an effective way to perform robust object tracking.
1 code implementation • 21 Apr 2021 • Jiqing Zhang, Chengjiang Long, Yuxin Wang, Haiyin Piao, Haiyang Mei, Xin Yang, BaoCai Yin
Recently, deep convolutional neural networks (CNNs) have been widely explored in single image super-resolution (SISR) and contribute remarkable progress.
no code implementations • 23 Aug 2019 • Xin Yang, Haiyang Mei, Jiqing Zhang, Ke Xu, Bao-Cai Yin, Qiang Zhang, Xiaopeng Wei
Recently, single-image super-resolution has made great progress owing to the development of deep convolutional neural networks (CNNs).