1 code implementation • 21 Mar 2023 • Yuxuan Han, Zhibo Wang, Feng Xu
This paper proposes the first 3D morphable face reflectance model with spatially varying BRDF using only low-cost publicly-available data.
1 code implementation • 25 Nov 2022 • Jingwang Ling, Zhibo Wang, Feng Xu
By supervising shadow rays, we successfully reconstruct a neural SDF of the scene from single-view images under multiple lighting conditions.
1 code implementation • 19 Jul 2022 • Jingwang Ling, Zhibo Wang, Ming Lu, Quan Wang, Chen Qian, Feng Xu
Previous works on morphable models mostly focus on large-scale facial geometry but ignore facial details.
no code implementations • 5 Jun 2022 • Guodong Cao, Zhibo Wang, Xiaowei Dong, Zhifei Zhang, Hengchang Guo, Zhan Qin, Kui Ren
However, most existing works are still trapped in the dilemma between higher accuracy and stronger robustness since they tend to fit a model towards robust features (not easily tampered with by adversaries) while ignoring those non-robust but highly predictive features.
1 code implementation • CVPR 2022 • Junfeng Lyu, Zhibo Wang, Feng Xu
In this paper, we propose a novel framework to remove eyeglasses as well as their cast shadows from face images.
no code implementations • CVPR 2022 • Zhibo Wang, Xiaowei Dong, Henry Xue, Zhifei Zhang, Weifeng Chiu, Tao Wei, Kui Ren
Prioritizing fairness is of central importance in artificial intelligence (AI) systems, especially for those societal applications, e. g., hiring systems should recommend applicants equally from different demographic groups, and risk assessment systems must eliminate racism in criminal justice.
no code implementations • 25 Feb 2022 • Feiliang Ren, Yongkang Liu, Bochao Li, Zhibo Wang, Yu Guo, Shilei Liu, Huimin Wu, Jiaqi Wang, Chunchao Liu, Bingchao Wang
Most existing multi-document machine reading comprehension models mainly focus on understanding the interactions between the input question and documents, but ignore following two kinds of understandings.
1 code implementation • ICCV 2021 • Zhibo Wang, Hengchang Guo, Zhifei Zhang, Wenxin Liu, Zhan Qin, Kui Ren
More specifically, we obtain feature importance by introducing the aggregate gradient, which averages the gradients with respect to feature maps of the source model, computed on a batch of random transforms of the original clean image.
no code implementations • 17 Nov 2019 • Zhibo Wang, Shen Yan, XiaoYu Zhang, Niels Lobo
(Very early draft)Traditional supervised learning keeps pushing convolution neural network(CNN) achieving state-of-art performance.
1 code implementation • ICCV 2019 • Zhibo Wang, Siyan Zheng, Mengkai Song, Qian Wang, Alireza Rahimpour, Hairong Qi
The results demonstrate that deep re-ID systems are vulnerable to our physical attacks.
no code implementations • 12 Feb 2019 • Wenqi Wang, Run Wang, Lina Wang, Zhibo Wang, Aoshuang Ye
Recently, studies have revealed adversarial examples in the text domain, which could effectively evade various DNN-based text analyzers and further bring the threats of the proliferation of disinformation.
1 code implementation • 3 Dec 2018 • Zhibo Wang, Mengkai Song, Zhifei Zhang, Yang song, Qian Wang, Hairong Qi
Although the state-of-the-art attacking techniques that incorporated the advance of Generative adversarial networks (GANs) could construct class representatives of the global data distribution among all clients, it is still challenging to distinguishably attack a specific client (i. e., user-level privacy leakage), which is a stronger privacy threat to precisely recover the private data from a specific client.