no code implementations • 25 Jun 2023 • Xiaoyan Guo, Jie Yang, Xinyu Jia, Chuanyan Zang, Yan Xu, Zhaoyang Chen
Therefore, this paper proposes a novel dual-pooling attention (DpA) module, which achieves the extraction and enhancement of locally important information about vehicles from both channel and spatial dimensions by constructing two branches of channel-pooling attention (CpA) and spatial-pooling attention (SpA), and employing multiple pooling operations to enhance the attention to fine-grained information of vehicles.
no code implementations • 10 Oct 2022 • Wanfeng Zheng, Qiang Li, Xiaoyan Guo, Pengfei Wan, Zhongyuan Wang
More specifically, our efforts consist of three parts: 1) a data-free training strategy to train latent mappers to bridge the latent space of CLIP and StyleGAN; 2) for more precise mapping, temporal relative consistency is proposed to address the knowledge distribution bias problem among different latent spaces; 3) to refine the mapped latent in s space, adaptive style mixing is also proposed.
no code implementations • 26 Aug 2022 • Zihui Wu, Haichang Gao, Bingqian Zhou, Xiaoyan Guo, Shudong Zhang
In addition, we discuss the function of entropy in TRADES, and we find that models with high entropy can be better robustness learners.
1 code implementation • CVPR 2022 • Xingyu Chen, Yufeng Liu, Yajiao Dong, Xiong Zhang, Chongyang Ma, Yanmin Xiong, Yuan Zhang, Xiaoyan Guo
In this work, we propose a framework for single-view hand mesh reconstruction, which can simultaneously achieve high reconstruction accuracy, fast inference speed, and temporal coherence.
Ranked #7 on 3D Hand Pose Estimation on DexYCB
no code implementations • 8 May 2021 • Yumeng Zhang, Li Chen, Yufeng Liu, Xiaoyan Guo, Wen Zheng, Junhai Yong
Deep learning methods have achieved excellent performance in pose estimation, but the lack of robustness causes the keypoints to change drastically between similar images.
1 code implementation • CVPR 2021 • Xingyu Chen, Yufeng Liu, Chongyang Ma, Jianlong Chang, Huayan Wang, Tian Chen, Xiaoyan Guo, Pengfei Wan, Wen Zheng
In the root-relative mesh recovery task, we exploit semantic relations among joints to generate a 3D mesh from the extracted 2D cues.
no code implementations • ECCV 2020 • Tian Chen, Shijie An, Yuan Zhang, Chongyang Ma, Huayan Wang, Xiaoyan Guo, Wen Zheng
Monocular depth estimation plays a crucial role in 3D recognition and understanding.