1.7k citations · 3.2k across the 31 of their papers we have counts for
33 papers
Neural Image Re-Exposure
Xinyu Zhang, Hefei Huang, Xu Jia +2
The shutter strategy applied to the photo-shooting process has a significant influence on the quality of the captured photograph. An improper shutter may lead to a blurry image, vi…
GM-NeRF: Learning Generalizable Model-based Neural Radiance Fields from Multi-view Images
Jianchuan Chen, Wentao Yi, Liqian Ma +2
In this work, we focus on synthesizing high-fidelity novel view images for arbitrary human performers, given a set of sparse multi-view images. It is a challenging task due to the…
AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image Enhancement
Canqian Yang, Meiguang Jin, Xu Jia +2
The 3D Lookup Table (3D LUT) is a highly-efficient tool for real-time image enhancement tasks, which models a non-linear 3D color transform by sparsely sampling it into a discretiz…
Look Back and Forth: Video Super-Resolution with Explicit Temporal Difference Modeling
Takashi Isobe, Xu Jia, Xin Tao +6
Temporal modeling is crucial for video super-resolution. Most of the video super-resolution methods adopt the optical flow or deformable convolution for explicitly motion compensat…
FoV-Net: Field-of-View Extrapolation Using Self-Attention and Uncertainty
Liqian Ma, Stamatios Georgoulis, Xu Jia +1
The ability to make educated predictions about their surroundings, and associate them with certain confidence, is important for intelligent systems, like autonomous vehicles and ro…
TimeReplayer: Unlocking the Potential of Event Cameras for Video Interpolation
Weihua He, Kaichao You, Zhendong Qiao +6
Recording fast motion in a high FPS (frame-per-second) requires expensive high-speed cameras. As an alternative, interpolating low-FPS videos from commodity cameras has attracted s…
Learning Enriched Illuminants for Cross and Single Sensor Color Constancy
Xiaodong Cun, Zhendong Wang, Chi-Man Pun +4
Color constancy aims to restore the constant colors of a scene under different illuminants. However, due to the existence of camera spectral sensitivity, the network trained on a c…
Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic Segmentation
Ruihuang Li, Shuai Li, Chenhang He +3
Domain adaptive semantic segmentation aims to learn a model with the supervision of source domain data, and produce satisfactory dense predictions on unlabeled target domain. One p…
Transformer-based Network for RGB-D Saliency Detection
Yue Wang, Xu Jia, Lu Zhang +3
RGB-D saliency detection integrates information from both RGB images and depth maps to improve prediction of salient regions under challenging conditions. The key to RGB-D saliency…
Motion Deblurring with Real Events
Fang Xu, Lei Yu, Bishan Wang +5
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 per…
Wavelet-Based Network For High Dynamic Range Imaging
Tianhong Dai, Wei Li, Xilei Cao +5
High dynamic range (HDR) imaging from multiple low dynamic range (LDR) images has been suffering from ghosting artifacts caused by scene and objects motion. Existing methods, such…
T-SVDNet: Exploring High-Order Prototypical Correlations for Multi-Source Domain Adaptation
Ruihuang Li, Xu Jia, Jianzhong He +2
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…
Multi-Target Domain Adaptation with Collaborative Consistency Learning
Takashi Isobe, Xu Jia, Shuaijun Chen +5
Recently unsupervised domain adaptation for the semantic segmentation task has become more and more popular due to high-cost of pixel-level annotation on real-world images. However…
Semi-supervised Domain Adaptation based on Dual-level Domain Mixing for Semantic Segmentation
Shuaijun Chen, Xu Jia, Jianzhong He +2
Data-driven based approaches, in spite of great success in many tasks, have poor generalization when applied to unseen image domains, and require expensive cost of annotation espec…
Multi-Source Domain Adaptation with Collaborative Learning for Semantic Segmentation
Jianzhong He, Xu Jia, Shuaijun Chen +1
Multi-source unsupervised domain adaptation~(MSDA) aims at adapting models trained on multiple labeled source domains to an unlabeled target domain. In this paper, we propose a nov…
Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images
Tao Huang, Songjiang Li, Xu Jia +2
In the last few years, image denoising has benefited a lot from the fast development of neural networks. However, the requirement of large amounts of noisy-clean image pairs for su…
Revisiting Temporal Modeling for Video Super-resolution
Takashi Isobe, Fang Zhu, Xu Jia +1
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 indus…
Video Super-Resolution with Recurrent Structure-Detail Network
Takashi Isobe, Xu Jia, Shuhang Gu +3
Most video super-resolution methods super-resolve a single reference frame with the help of neighboring frames in a temporal sliding window. They are less efficient compared to the…
Video Super-resolution with Temporal Group Attention
Takashi Isobe, Songjiang Li, Xu Jia +6
Video super-resolution, which aims at producing a high-resolution video from its corresponding low-resolution version, has recently drawn increasing attention. In this work, we pro…
Learning to Select Base Classes for Few-shot Classification
Linjun Zhou, Peng Cui, Xu Jia +2
Few-shot learning has attracted intensive research attention in recent years. Many methods have been proposed to generalize a model learned from provided base classes to novel clas…
Unsupervised Model Personalization while Preserving Privacy and Scalability: An Open Problem
Matthias De Lange, Xu Jia, Sarah Parisot +3
This work investigates the task of unsupervised model personalization, adapted to continually evolving, unlabeled local user images. We consider the practical scenario where a high…
Unsupervised Image Super-Resolution with an Indirect Supervised Path
Zhen Han, Enyan Dai, Xu Jia +5
The task of single image super-resolution (SISR) aims at reconstructing a high-resolution (HR) image from a low-resolution (LR) image. Although significant progress has been made b…
Efficient Residual Dense Block Search for Image Super-Resolution
Dehua Song, Chang Xu, Xu Jia +3
Although remarkable progress has been made on single image super-resolution due to the revival of deep convolutional neural networks, deep learning methods are confronted with the…
A continual learning survey: Defying forgetting in classification tasks
Matthias De Lange, Rahaf Aljundi, Marc Masana +5
Artificial neural networks thrive in solving the classification problem for a particular rigid task, acquiring knowledge through generalized learning behaviour from a distinct trai…