activity
20162022
most citedMeta-SR: A Magnification-Arbitrary Network for Super-Resolution

44 citations · 46 across the 3 of their papers we have counts for

collaborators

7 papers

cs.CV20222 cited

Low-confidence Samples Matter for Domain Adaptation

Yixin Zhang, Junjie Li, Zilei Wang

Domain adaptation (DA) aims to transfer knowledge from a label-rich source domain to a related but label-scarce target domain. The conventional DA strategy is to align the feature…

cs.CV2021

Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images

Wentao Chen, Chenyang Si, Wei Wang +3

Few-shot learning is a challenging task since only few instances are given for recognizing an unseen class. One way to alleviate this problem is to acquire a strong inductive bias…

cs.CV2020

Video Semantic Segmentation with Distortion-Aware Feature Correction

Jiafan Zhuang, Zilei Wang, Bingke Wang

Video semantic segmentation is active in recent years benefited from the great progress of image semantic segmentation. For such a task, the per-frame image segmentation is general…

cs.CV2019

Context-Aware Dynamic Feature Extraction for 3D Object Detection in Point Clouds

Yonglin Tian, Lichao Huang, Xuesong Li +3

Varying density of point clouds increases the difficulty of 3D detection. In this paper, we present a context-aware dynamic network (CADNet) to capture the variance of density by c…

cs.CV2019

Adaptive and Azimuth-Aware Fusion Network of Multimodal Local Features for 3D Object Detection

Yonglin Tian, Kunfeng Wang, Yuang Wang +3

This paper focuses on the construction of stronger local features and the effective fusion of image and LiDAR data. We adopt different modalities of LiDAR data to generate richer f…

cs.CV201944 cited

Meta-SR: A Magnification-Arbitrary Network for Super-Resolution

Xuecai Hu, Haoyuan Mu, Xiangyu Zhang +3

Recent research on super-resolution has achieved great success due to the development of deep convolutional neural networks (DCNNs). However, super-resolution of arbitrary scale fa…