Search Results for author: Zhiwu Huang

Found 37 papers, 13 papers with code

S-Prompts Learning with Pre-trained Transformers: An Occam's Razor for Domain Incremental Learning

1 code implementation26 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).

Continual Learning Incremental Learning

Multi-agent Actor-Critic with Time Dynamical Opponent Model

no code implementations12 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.

Multi-agent Reinforcement Learning

Neural Architecture Search for Efficient Uncalibrated Deep Photometric Stereo

no code implementations11 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.

Neural Architecture Search

GANmut: Learning Interpretable Conditional Space for Gamut of Emotions

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.

Generative Flows with Invertible Attentions

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.

Image Generation

Direct Differentiable Augmentation Search

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.

AutoML Data Augmentation +4

Trilevel Neural Architecture Search for Efficient Single Image Super-Resolution

no code implementations17 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.

Image Super-Resolution Neural Architecture Search +1

An Efficient Recurrent Adversarial Framework for Unsupervised Real-Time Video Enhancement

no code implementations24 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.

Video Enhancement

Neural Architecture Search of SPD Manifold Networks

1 code implementation27 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.

Emotion Recognition Neural Architecture Search

Facial Emotion Recognition with Noisy Multi-task Annotations

1 code implementation19 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.

Facial Emotion Recognition

Neural Architecture Search as Sparse Supernet

no code implementations31 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.

Neural Architecture Search

Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture 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.

Image Generation Neural Architecture Search +1

Divide-and-Conquer Adversarial Learning for High-Resolution Image and Video Enhancement

no code implementations23 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.

Video Enhancement

Sliced Wasserstein Generative Models

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.

Image Generation Video Generation

Towards High Resolution Video Generation with Progressive Growing of Sliced Wasserstein GANs

1 code implementation4 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.

Action Recognition Image Generation +1

Covariance Pooling For Facial Expression Recognition

1 code implementation13 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

Manifold-valued Image Generation with Wasserstein Generative Adversarial Nets

no code implementations5 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.

Image Generation

Face Translation between Images and Videos using Identity-aware CycleGAN

no code implementations4 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.

Image-to-Image Translation Translation +1

Wasserstein Divergence for GANs

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.

Image Generation

Improving Video Generation for Multi-functional Applications

1 code implementation30 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.

Colorization Future prediction +2

Sliced Wasserstein Generative Models

1 code implementation8 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.

Image Generation Video Generation

On the Relation between Color Image Denoising and Classification

no code implementations5 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.

Classification Color Image Denoising +2

Building Deep Networks on Grassmann Manifolds

no code implementations17 Nov 2016 Zhiwu Huang, Jiqing Wu, Luc van Gool

Learning representations on Grassmann manifolds is popular in quite a few visual recognition tasks.

Geometry-aware Similarity Learning on SPD Manifolds for Visual Recognition

no code implementations17 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.

Cross Euclidean-to-Riemannian Metric Learning with Application to Face Recognition from Video

no code implementations15 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.

Face Recognition Metric Learning

A Riemannian Network for SPD Matrix Learning

no code implementations15 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.

Face Video Retrieval With Image Query via Hashing Across Euclidean Space and Riemannian 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.

Video Retrieval

Projection Metric Learning on Grassmann Manifold With Application to Video Based Face Recognition

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.

Dimensionality Reduction Face Recognition +1

Discriminant Analysis on Riemannian Manifold of Gaussian Distributions for Face Recognition With Image Sets

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.

Face Identification Face Recognition +1

Learning Euclidean-to-Riemannian Metric for Point-to-Set Classification

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.

Classification General Classification +1

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