Search Results for author: Xu Jia

Found 28 papers, 14 papers with code

More Classifiers, Less Forgetting: A Generic Multi-classifier Paradigm for Incremental Learning

1 code implementation ECCV 2020 Yu Liu, Sarah Parisot, Gregory Slabaugh, Xu Jia, Ales Leonardis, Tinne Tuytelaars

Since those regularization strategies are mostly associated with classifier outputs, we propose a MUlti-Classifier (MUC) incremental learning paradigm that integrates an ensemble of auxiliary classifiers to estimate more effective regularization constraints.

Incremental Learning

Motion Deblurring with Real Events

no code implementations ICCV 2021 Fang Xu, Lei Yu, Bishan Wang, Wen Yang, Gui-Song Xia, Xu Jia, Zhendong Qiao, Jianzhuang Liu

In this paper, we propose an end-to-end learning framework for event-based motion deblurring in a self-supervised manner, where real-world events are exploited to alleviate the performance degradation caused by data inconsistency.

Deblurring

Wavelet-Based Network For High Dynamic Range Imaging

no code implementations3 Aug 2021 Tianhong Dai, Wei Li, Xilei Cao, Jianzhuang Liu, Xu Jia, Ales Leonardis, Youliang Yan, Shanxin Yuan

The frequency-guided upsampling module reconstructs details from multiple frequency-specific components with rich details.

Optical Flow Estimation

T-SVDNet: Exploring High-Order Prototypical Correlations for Multi-Source Domain Adaptation

1 code implementation ICCV 2021 Ruihuang Li, Xu Jia, Jianzhong He, Shuaijun Chen, QinGhua Hu

Most existing domain adaptation methods focus on adaptation from only one source domain, however, in practice there are a number of relevant sources that could be leveraged to help improve performance on target domain.

Domain Adaptation

Animatable Neural Radiance Fields from Monocular RGB Videos

1 code implementation25 Jun 2021 Jianchuan Chen, Ying Zhang, Di Kang, Xuefei Zhe, Linchao Bao, Xu Jia, Huchuan Lu

We present animatable neural radiance fields (animatable NeRF) for detailed human avatar creation from monocular videos.

3D Human Reconstruction Neural Rendering +2

Multi-Target Domain Adaptation with Collaborative Consistency Learning

no code implementations CVPR 2021 Takashi Isobe, Xu Jia, Shuaijun Chen, Jianzhong He, Yongjie Shi, Jianzhuang Liu, Huchuan Lu, Shengjin Wang

To obtain a single model that works across multiple target domains, we propose to simultaneously learn a student model which is trained to not only imitate the output of each expert on the corresponding target domain, but also to pull different expert close to each other with regularization on their weights.

Multi-target Domain Adaptation Semantic Segmentation +1

Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images

3 code implementations CVPR 2021 Tao Huang, Songjiang Li, Xu Jia, Huchuan Lu, Jianzhuang Liu

In this paper, we present a very simple yet effective method named Neighbor2Neighbor to train an effective image denoising model with only noisy images.

Image Denoising Self-Supervised Learning

Revisiting Temporal Modeling for Video Super-resolution

1 code implementation13 Aug 2020 Takashi Isobe, Fang Zhu, Xu Jia, Shengjin Wang

Video super-resolution plays an important role in surveillance video analysis and ultra-high-definition video display, which has drawn much attention in both the research and industrial communities.

Video Super-Resolution

Video Super-resolution with Temporal Group Attention

1 code implementation CVPR 2020 Takashi Isobe, Songjiang Li, Xu Jia, Shanxin Yuan, Gregory Slabaugh, Chunjing Xu, Ya-Li Li, Shengjin Wang, Qi Tian

Video super-resolution, which aims at producing a high-resolution video from its corresponding low-resolution version, has recently drawn increasing attention.

Video Super-Resolution

Unsupervised Model Personalization while Preserving Privacy and Scalability: An Open Problem

1 code implementation CVPR 2020 Matthias De Lange, Xu Jia, Sarah Parisot, Ales Leonardis, Gregory Slabaugh, Tinne Tuytelaars

This framework flexibly disentangles user-adaptation into model personalization on the server and local data regularization on the user device, with desirable properties regarding scalability and privacy constraints.

Continual Learning Domain Adaptation +2

MMD GAN with Random-Forest Kernels

no code implementations ICLR 2020 Tao Huang, Zhen Han, Xu Jia, Hanyuan Hang

In this paper, we propose a novel kind of kernel, random forest kernel, to enhance the empirical performance of MMD GAN.

Ensemble Learning

Unsupervised Image Super-Resolution with an Indirect Supervised Path

no code implementations7 Oct 2019 Zhen Han, Enyan Dai, Xu Jia, Xiaoying Ren, Shuaijun Chen, Chunjing Xu, Jianzhuang Liu, Qi Tian

The task of single image super-resolution (SISR) aims at reconstructing a high-resolution (HR) image from a low-resolution (LR) image.

Image Super-Resolution Translation

Efficient Residual Dense Block Search for Image Super-Resolution

no code implementations25 Sep 2019 Dehua Song, Chang Xu, Xu Jia, Yiyi Chen, Chunjing Xu, Yunhe Wang

Focusing on this issue, we propose an efficient residual dense block search algorithm with multiple objectives to hunt for fast, lightweight and accurate networks for image super-resolution.

Image Super-Resolution

A continual learning survey: Defying forgetting in classification tasks

1 code implementation18 Sep 2019 Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Ales Leonardis, Gregory Slabaugh, Tinne Tuytelaars

Artificial neural networks thrive in solving the classification problem for a particular rigid task, acquiring knowledge through generalized learning behaviour from a distinct training phase.

Classification Continual Learning +2

Co-Evolutionary Compression for Unpaired Image Translation

1 code implementation ICCV 2019 Han Shu, Yunhe Wang, Xu Jia, Kai Han, Hanting Chen, Chunjing Xu, Qi Tian, Chang Xu

Generative adversarial networks (GANs) have been successfully used for considerable computer vision tasks, especially the image-to-image translation.

Image-to-Image Translation Translation

Video Generation from Single Semantic Label Map

2 code implementations CVPR 2019 Junting Pan, Chengyu Wang, Xu Jia, Jing Shao, Lu Sheng, Junjie Yan, Xiaogang Wang

This paper proposes the novel task of video generation conditioned on a SINGLE semantic label map, which provides a good balance between flexibility and quality in the generation process.

Image Generation Optical Flow Estimation +1

Exemplar Guided Unsupervised Image-to-Image Translation with Semantic Consistency

no code implementations ICLR 2019 Liqian Ma, Xu Jia, Stamatios Georgoulis, Tinne Tuytelaars, Luc van Gool

Experimental results on various datasets show that EGSC-IT does not only translate the source image to diverse instances in the target domain, but also preserves the semantic consistency during the process.

Translation Unsupervised Image-To-Image Translation

Super-Resolution with Deep Adaptive Image Resampling

no code implementations18 Dec 2017 Xu Jia, Hong Chang, Tinne Tuytelaars

In this work, we revisit the more traditional interpolation-based methods, that were popular before, now with the help of deep learning.

Image Super-Resolution

Pose Guided Person Image Generation

2 code implementations NeurIPS 2017 Liqian Ma, Xu Jia, Qianru Sun, Bernt Schiele, Tinne Tuytelaars, Luc van Gool

This paper proposes the novel Pose Guided Person Generation Network (PG$^2$) that allows to synthesize person images in arbitrary poses, based on an image of that person and a novel pose.

Gesture-to-Gesture Translation Pose Transfer

Dynamic Filter Networks

1 code implementation NeurIPS 2016 Bert De Brabandere, Xu Jia, Tinne Tuytelaars, Luc van Gool

In a traditional convolutional layer, the learned filters stay fixed after training.

Depth Estimation Optical Flow Estimation

Towards Automatic Image Editing: Learning to See another You

no code implementations26 Nov 2015 Amir Ghodrati, Xu Jia, Marco Pedersoli, Tinne Tuytelaars

Learning the distribution of images in order to generate new samples is a challenging task due to the high dimensionality of the data and the highly non-linear relations that are involved.

Image Generation

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