no code implementations • 15 Apr 2024 • Han Xue, Qianru Sun, Li Song, Wenjun Zhang, Zhiwu Huang
Secondly, it standardizes the training of different tasks into a general in-context learning, where "in-context" means the input comprises an example input-output pair of the target task and a query image.
1 code implementation • 4 Jan 2024 • Yabin Wang, Zhiwu Huang, Zhiheng Ma, Xiaopeng Hong
The two distinguished features enable DFLIP-3K to develop a benchmark that promotes progress in linguistic profiling of deepfakes, which includes three sub-tasks namely deepfake detection, model identification, and prompt prediction.
no code implementations • 6 Aug 2023 • Yue Wu, Zhiwu Huang, Dongjun Li, Heng Li, Jun Peng, Daniel Stroe, Ziyou Song
A control-oriented onboard BTMS model is proposed and verified under different speed profiles and temperatures.
no code implementations • 26 Mar 2023 • Ziheng Chen, Yue Song, Tianyang Xu, Zhiwu Huang, Xiao-Jun Wu, Nicu Sebe
Symmetric Positive Definite (SPD) matrices have received wide attention in machine learning due to their intrinsic capacity of encoding underlying structural correlation in data.
1 code implementation • CVPR 2023 • Han Xue, Zhiwu Huang, Qianru Sun, Li Song, Wenjun Zhang
In this work, we explore the freestyle capability of the model, i. e., how far can it generate unseen semantics (e. g., classes, attributes, and styles) onto a given layout, and call the task Freestyle LIS (FLIS).
no code implementations • 28 Feb 2023 • Yabin Wang, Zhiwu Huang, Xiaopeng Hong
To address potentially appeared ethics questions, this paper establishes a deepart detection database (DDDB) that consists of a set of high-quality conventional art images (conarts) and five sets of deepart images generated by five state-of-the-art deepfake models.
1 code implementation • 29 Nov 2022 • Yabin Wang, Zhiheng Ma, Zhiwu Huang, YaoWei Wang, Zhou Su, Xiaopeng Hong
To avoid obvious stage learning bottlenecks, we propose a brand-new stage-isolation based incremental learning framework, which leverages a series of stage-isolated classifiers to perform the learning task of each stage without the interference of others.
2 code implementations • 26 Jul 2022 • Yabin Wang, Zhiwu Huang, Xiaopeng Hong
In this paper, we propose one simple paradigm (named as S-Prompting) and two concrete approaches to highly reduce the forgetting degree in one of the most typical continual learning scenarios, i. e., domain increment learning (DIL).
1 code implementation • 11 May 2022 • Chuqiao Li, Zhiwu Huang, Danda Pani Paudel, Yabin Wang, Mohamad Shahbazi, Xiaopeng Hong, Luc van Gool
Within the proposed benchmark, we explore some commonly known essentials of standard continual learning.
no code implementations • 12 Apr 2022 • Yuan Tian, Klaus-Rudolf Kladny, Qin Wang, Zhiwu Huang, Olga Fink
In this paper, we propose to exploit the fact that the agents seek to improve their expected cumulative reward and introduce a novel \textit{Time Dynamical Opponent Model} (TDOM) to encode the knowledge that the opponent policies tend to improve over time.
1 code implementation • 25 Jan 2022 • Ziheng Chen, Tianyang Xu, Xiao-Jun Wu, Rui Wang, Zhiwu Huang, Josef Kittler
The Symmetric Positive Definite (SPD) matrices have received wide attention for data representation in many scientific areas.
no code implementations • 11 Oct 2021 • Francesco Sarno, Suryansh Kumar, Berk Kaya, Zhiwu Huang, Vittorio Ferrari, Luc van Gool
We then perform a continuous relaxation of this search space and present a gradient-based optimization strategy to find an efficient light calibration and normal estimation network.
no code implementations • CVPR 2021 • Stefano d'Apolito, Danda Pani Paudel, Zhiwu Huang, Andres Romero, Luc van Gool
On the other hand, learning from inexpensive and intuitive basic categorical emotion labels leads to limited emotion variability.
no code implementations • CVPR 2022 • Rhea Sanjay Sukthanker, Zhiwu Huang, Suryansh Kumar, Radu Timofte, Luc van Gool
The key idea is to exploit a masked scheme of these two attentions to learn long-range data dependencies in the context of generative flows.
1 code implementation • ICCV 2021 • Aoming Liu, Zehao Huang, Zhiwu Huang, Naiyan Wang
Data augmentation has been an indispensable tool to improve the performance of deep neural networks, however the augmentation can hardly transfer among different tasks and datasets.
1 code implementation • 7 Mar 2021 • Anton Obukhov, Maxim Rakhuba, Alexander Liniger, Zhiwu Huang, Stamatios Georgoulis, Dengxin Dai, Luc van Gool
We study low-rank parameterizations of weight matrices with embedded spectral properties in the Deep Learning context.
1 code implementation • CVPR 2021 • Mohamad Shahbazi, Zhiwu Huang, Danda Pani Paudel, Ajad Chhatkuli, Luc van Gool
To address this problem, we introduce a new GAN transfer method to explicitly propagate the knowledge from the old classes to the new classes.
no code implementations • 17 Jan 2021 • Yan Wu, Zhiwu Huang, Suryansh Kumar, Rhea Sanjay Sukthanker, Radu Timofte, Luc van Gool
Modern solutions to the single image super-resolution (SISR) problem using deep neural networks aim not only at better performance accuracy but also at a lighter and computationally efficient model.
no code implementations • 24 Dec 2020 • Dario Fuoli, Zhiwu Huang, Danda Pani Paudel, Luc van Gool, Radu Timofte
Video enhancement is a challenging problem, more than that of stills, mainly due to high computational cost, larger data volumes and the difficulty of achieving consistency in the spatio-temporal domain.
1 code implementation • 27 Oct 2020 • Rhea Sanjay Sukthanker, Zhiwu Huang, Suryansh Kumar, Erik Goron Endsjo, Yan Wu, Luc van Gool
To address this problem, we first introduce a geometrically rich and diverse SPD neural architecture search space for an efficient SPD cell design.
1 code implementation • 19 Oct 2020 • Siwei Zhang, Zhiwu Huang, Danda Pani Paudel, Luc van Gool
In our formulation, we exploit a new method to enable the emotion prediction and the joint distribution learning in a unified adversarial learning game.
no code implementations • 14 Sep 2020 • Dario Fuoli, Zhiwu Huang, Shuhang Gu, Radu Timofte, Arnau Raventos, Aryan Esfandiari, Salah Karout, Xuan Xu, Xin Li, Xin Xiong, Jinge Wang, Pablo Navarrete Michelini, Wen-Hao Zhang, Dongyang Zhang, Hanwei Zhu, Dan Xia, Haoyu Chen, Jinjin Gu, Zhi Zhang, Tongtong Zhao, Shanshan Zhao, Kazutoshi Akita, Norimichi Ukita, Hrishikesh P. S, Densen Puthussery, Jiji C. V
Missing information can be restored well in this region, especially in HR videos, where the high-frequency content mostly consists of texture details.
no code implementations • 31 Jul 2020 • Yan Wu, Aoming Liu, Zhiwu Huang, Siwei Zhang, Luc van Gool
This paper aims at enlarging the problem of Neural Architecture Search (NAS) from Single-Path and Multi-Path Search to automated Mixed-Path Search.
1 code implementation • ECCV 2020 • Yuan Tian, Qin Wang, Zhiwu Huang, Wen Li, Dengxin Dai, Minghao Yang, Jun Wang, Olga Fink
In this paper, we introduce a new reinforcement learning (RL) based neural architecture search (NAS) methodology for effective and efficient generative adversarial network (GAN) architecture search.
Ranked #13 on Image Generation on STL-10
no code implementations • 5 May 2020 • Dario Fuoli, Zhiwu Huang, Martin Danelljan, Radu Timofte, Hua Wang, Longcun Jin, Dewei Su, Jing Liu, Jaehoon Lee, Michal Kudelski, Lukasz Bala, Dmitry Hrybov, Marcin Mozejko, Muchen Li, Si-Yao Li, Bo Pang, Cewu Lu, Chao Li, Dongliang He, Fu Li, Shilei Wen
For track 2, some existing methods are evaluated, showing promising solutions to the weakly-supervised video quality mapping problem.
no code implementations • 23 Oct 2019 • Zhiwu Huang, Danda Pani Paudel, Guanju Li, Jiqing Wu, Radu Timofte, Luc van Gool
This paper introduces a divide-and-conquer inspired adversarial learning (DACAL) approach for photo enhancement.
1 code implementation • CVPR 2019 • Jiqing Wu, Zhiwu Huang, Dinesh Acharya, Wen Li, Janine Thoma, Danda Pani Paudel, Luc van Gool
In generative modeling, the Wasserstein distance (WD) has emerged as a useful metric to measure the discrepancy between generated and real data distributions.
Ranked #1 on Video Generation on TrailerFaces
1 code implementation • 4 Oct 2018 • Dinesh Acharya, Zhiwu Huang, Danda Pani Paudel, Luc van Gool
Furthermore, we introduce a sliced version of Wasserstein GAN (SWGAN) loss to improve the distribution learning on the video data of high-dimension and mixed-spatiotemporal distribution.
1 code implementation • 13 May 2018 • Dinesh Acharya, Zhiwu Huang, Danda Paudel, Luc van Gool
In this work, we explore the benefits of using a man- ifold network structure for covariance pooling to improve facial expression recognition.
Facial Expression Recognition Facial Expression Recognition (FER)
no code implementations • 5 Dec 2017 • Zhiwu Huang, Jiqing Wu, Luc van Gool
In addition, we recommend three benchmark datasets that are CIFAR-10 HSV/CB color images, ImageNet HSV/CB color images, UCL DT image datasets.
1 code implementation • ECCV 2018 • Jiqing Wu, Zhiwu Huang, Janine Thoma, Dinesh Acharya, Luc van Gool
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-of-the-art due to the theoretical contributions and competitive qualitative performance.
no code implementations • 4 Dec 2017 • Zhiwu Huang, Bernhard Kratzwald, Danda Pani Paudel, Jiqing Wu, Luc van Gool
This paper presents a new problem of unpaired face translation between images and videos, which can be applied to facial video prediction and enhancement.
1 code implementation • 30 Nov 2017 • Bernhard Kratzwald, Zhiwu Huang, Danda Pani Paudel, Acharya Dinesh, Luc van Gool
In this paper, we aim to improve the state-of-the-art video generative adversarial networks (GANs) with a view towards multi-functional applications.
1 code implementation • 8 Jun 2017 • Jiqing Wu, Zhiwu Huang, Dinesh Acharya, Wen Li, Janine Thoma, Danda Pani Paudel, Luc van Gool
In generative modeling, the Wasserstein distance (WD) has emerged as a useful metric to measure the discrepancy between generated and real data distributions.
no code implementations • 5 Apr 2017 • Jiqing Wu, Radu Timofte, Zhiwu Huang, Luc van Gool
Inspired by classification models, we propose a novel deep learning architecture for color (multichannel) image denoising and report on thousands of images from ImageNet dataset as well as commonly used imagery.
no code implementations • CVPR 2017 • Zhiwu Huang, Chengde Wan, Thomas Probst, Luc van Gool
In recent years, skeleton-based action recognition has become a popular 3D classification problem.
no code implementations • 17 Nov 2016 • Zhiwu Huang, Jiqing Wu, Luc van Gool
Learning representations on Grassmann manifolds is popular in quite a few visual recognition tasks.
no code implementations • 17 Aug 2016 • Zhiwu Huang, Ruiping Wang, Xianqiu Li, Wenxian Liu, Shiguang Shan, Luc van Gool, Xilin Chen
Specifically, by exploiting the Riemannian geometry of the manifold of fixed-rank Positive Semidefinite (PSD) matrices, we present a new solution to reduce optimizing over the space of column full-rank transformation matrices to optimizing on the PSD manifold which has a well-established Riemannian structure.
no code implementations • 15 Aug 2016 • Zhiwu Huang, Ruiping Wang, Shiguang Shan, Luc van Gool, Xilin Chen
With this mapping, the problem of learning a cross-view metric between the two source heterogeneous spaces can be expressed as learning a single-view Euclidean distance metric in the target common Euclidean space.
no code implementations • 15 Aug 2016 • 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 representations while respecting Riemannian geometry of underlying SPD manifolds.
no code implementations • CVPR 2015 • Zhiwu Huang, Ruiping Wang, Shiguang Shan, Xilin Chen
In video based face recognition, great success has been made by representing videos as linear subspaces, which typically lie in a special type of non-Euclidean space known as Grassmann manifold.
no code implementations • CVPR 2015 • Yan Li, Ruiping Wang, Zhiwu Huang, Shiguang Shan, Xilin Chen
Retrieving videos of a specific person given his/her face image as query becomes more and more appealing for applications like smart movie fast-forwards and suspect searching.
no code implementations • CVPR 2015 • Wen Wang, Ruiping Wang, Zhiwu Huang, Shiguang Shan, Xilin Chen
This paper presents a method named Discriminant Analysis on Riemannian manifold of Gaussian distributions (DARG) to solve the problem of face recognition with image sets.
no code implementations • CVPR 2014 • Zhiwu Huang, Ruiping Wang, Shiguang Shan, Xilin Chen
Since the points commonly lie in Euclidean space while the sets are typically modeled as elements on Riemannian manifold, they can be treated as Euclidean points and Riemannian points respectively.