119 citations · 287 across the 16 of their papers we have counts for
13 papers · 1 filter
Benchmarking Deepart Detection
Yabin Wang, Zhiwu Huang, Xiaopeng Hong
Deepfake technologies have been blurring the boundaries between the real and unreal, likely resulting in malicious events. By leveraging newly emerged deepfake technologies, deepfa…
S-Prompts Learning with Pre-trained Transformers: An Occam's Razor for Domain Incremental Learning
Yabin Wang, Zhiwu Huang, Xiaopeng Hong
State-of-the-art deep neural networks are still struggling to address the catastrophic forgetting problem in continual learning. In this paper, we propose one simple paradigm (name…
A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials
Chuqiao Li, Zhiwu Huang, Danda Pani Paudel +4
There have been emerging a number of benchmarks and techniques for the detection of deepfakes. However, very few works study the detection of incrementally appearing deepfakes in t…
Manifold-valued Image Generation with Wasserstein Generative Adversarial Nets
Zhiwu Huang, Jiqing Wu, Luc Van Gool
Generative modeling over natural images is one of the most fundamental machine learning problems. However, few modern generative models, including Wasserstein Generative Adversaria…
Wasserstein Divergence for GANs
Jiqing Wu, Zhiwu Huang, Janine Thoma +2
In many domains of computer vision, generative adversarial networks (GANs) have achieved great success, among which the family of Wasserstein GANs (WGANs) is considered to be state…
Improving Video Generation for Multi-functional Applications
Bernhard Kratzwald, Zhiwu Huang, Danda Pani Paudel +2
In this paper, we aim to improve the state-of-the-art video generative adversarial networks (GANs) with a view towards multi-functional applications. Our improved video GAN model d…
Sliced Wasserstein Generative Models
Jiqing Wu, Zhiwu Huang, Dinesh Acharya +4
In generative modeling, the Wasserstein distance (WD) has emerged as a useful metric to measure the discrepancy between generated and real data distributions. Unfortunately, it is…
On the Relation between Color Image Denoising and Classification
Jiqing Wu, Radu Timofte, Zhiwu Huang +1
Large amount of image denoising literature focuses on single channel images and often experimentally validates the proposed methods on tens of images at most. In this paper, we inv…
Deep Learning on Lie Groups for Skeleton-based Action Recognition
Zhiwu Huang, Chengde Wan, Thomas Probst +1
In recent years, skeleton-based action recognition has become a popular 3D classification problem. State-of-the-art methods typically first represent each motion sequence as a high…
Building Deep Networks on Grassmann Manifolds
Zhiwu Huang, Jiqing Wu, Luc Van Gool
Learning representations on Grassmann manifolds is popular in quite a few visual recognition tasks. In order to enable deep learning on Grassmann manifolds, this paper proposes a d…
Geometry-aware Similarity Learning on SPD Manifolds for Visual Recognition
Zhiwu Huang, Ruiping Wang, Xianqiu Li +4
Symmetric Positive Definite (SPD) matrices have been widely used for data representation in many visual recognition tasks. The success mainly attributes to learning discriminative…
A Riemannian Network for SPD Matrix Learning
Zhiwu Huang, Luc Van Gool
Symmetric Positive Definite (SPD) matrix learning methods have become popular in many image and video processing tasks, thanks to their ability to learn appropriate statistical rep…
Cross Euclidean-to-Riemannian Metric Learning with Application to Face Recognition from Video
Zhiwu Huang, Ruiping Wang, Shiguang Shan +2
Riemannian manifolds have been widely employed for video representations in visual classification tasks including video-based face recognition. The success mainly derives from lear…