1.7k citations · 3.2k across the 31 of their papers we have counts for
8 papers · 1 filter
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…