no code implementations • CVPR 2023 • Shibin Mei, Chenglong Zhao, Shengchao Yuan, Bingbing Ni
In this paper, we identify pattern imbalance from several aspects, and further develop a new training scheme to avert pattern preference as well as spurious correlation.
no code implementations • 9 Sep 2021 • Ruoxi Shi, Borui Yang, Yangzhou Jiang, Chenglong Zhao, Bingbing Ni
Base on the eigenvalues, we can model the energy distribution of adversarial perturbations.
no code implementations • ICCV 2021 • Zhenbo Yu, Bingbing Ni, Jingwei Xu, Junjie Wang, Chenglong Zhao, Wenjun Zhang
Furthermore, two temporal constraints are proposed to alleviate the scale and pose ambiguity respectively.
Monocular 3D Human Pose Estimation Unsupervised 3D Human Pose Estimation
no code implementations • ICCV 2021 • Zhenbo Yu, Junjie Wang, Jingwei Xu, Bingbing Ni, Chenglong Zhao, Minsi Wang, Wenjun Zhang
The challenges of the latter task are two folds: (1) pose failure (i. e., pose mismatching -- different skeleton definitions in dataset and SMPL , and pose ambiguity -- endpoints have arbitrary joint angle configurations for the same 3D joint coordinates).
1 code implementation • NeurIPS 2020 • Jiancheng Yang, Yangzhou Jiang, Xiaoyang Huang, Bingbing Ni, Chenglong Zhao
This paper addresses the challenging black-box adversarial attack problem, where only classification confidence of a victim model is available.
no code implementations • 6 Aug 2019 • Yunxiang Zhang, Chenglong Zhao, Bingbing Ni, Jian Zhang, Haoran Deng
To address the limitations of existing magnitude-based pruning algorithms in cases where model weights or activations are of large and similar magnitude, we propose a novel perspective to discover parameter redundancy among channels and accelerate deep CNNs via channel pruning.
no code implementations • CVPR 2019 • Chenglong Zhao, Bingbing Ni, Jian Zhang, Qiwei Zhao, Wenjun Zhang, Qi Tian
We propose a variational Bayesian scheme for pruning convolutional neural networks in channel level.