no code implementations • 31 May 2024 • You Li, Guannan Zhao, Shuyu Kong, Yunqi He, Hai Zhou
This paper presents a systematic and efficient method to evaluate and verify global robustness for deep neural networks, leveraging the PAC verification framework for solid guarantees on verification results.
no code implementations • 4 Oct 2022 • Honghu Pan, Yongyong Chen, Yunqi He, Xin Li, Zhenyu He
To this end, we propose Flow2Flow, a unified framework that could jointly achieve training sample expansion and cross-modality image generation for V2I person ReID.
no code implementations • 23 Sep 2022 • Honghu Pan, Qiao Liu, Yongyong Chen, Yunqi He, Yuan Zheng, Feng Zheng, Zhenyu He
Finally, we propose a dual-attention method consisting of node-attention and time-attention to obtain the temporal graph representation from the node embeddings, where the self-attention mechanism is employed to learn the importance of each node and each frame.
no code implementations • 23 Sep 2022 • Honghu Pan, Yongyong Chen, Tingyang Xu, Yunqi He, Zhenyu He
Extensive experiments on two large gait recognition datasets, i. e., CASIA-B and OUMVLP-Pose, demonstrate that our method outperforms the baseline model and existing pose-based methods by a large margin.