Search Results for author: Zhibo Wang

Found 12 papers, 7 papers with code

Learning a 3D Morphable Face Reflectance Model from Low-cost Data

1 code implementation21 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.

Face Model

ShadowNeuS: Neural SDF Reconstruction by Shadow Ray Supervision

1 code implementation25 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.

Novel View Synthesis

Structure-aware Editable Morphable Model for 3D Facial Detail Animation and Manipulation

1 code implementation19 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.

Vanilla Feature Distillation for Improving the Accuracy-Robustness Trade-Off in Adversarial Training

no code implementations5 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.

Knowledge Distillation

Portrait Eyeglasses and Shadow Removal by Leveraging 3D Synthetic Data

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.

Face Verification Shadow Removal

Fairness-aware Adversarial Perturbation Towards Bias Mitigation for Deployed Deep Models

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.


Deep Understanding based Multi-Document Machine Reading Comprehension

no code implementations25 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.

Machine Reading Comprehension TriviaQA

Feature Importance-aware Transferable Adversarial Attacks

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.

Feature Importance

Unsupervised Visual Representation Learning with Increasing Object Shape Bias

no code implementations17 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.

Representation Learning

Towards a Robust Deep Neural Network in Texts: A Survey

no code implementations12 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.

General Classification Image Classification +2

Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning

1 code implementation3 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.

Edge-computing Federated Learning +1

Cannot find the paper you are looking for? You can Submit a new open access paper.