activity
20182021
most citedWord-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison

53 citations · 122 across the 13 of their papers we have counts for

collaborators

25 papers

cs.CV20212 cited

DSC-PoseNet: Learning 6DoF Object Pose Estimation via Dual-scale Consistency

Zongxin Yang, Xin Yu, Yi Yang

Compared to 2D object bounding-box labeling, it is very difficult for humans to annotate 3D object poses, especially when depth images of scenes are unavailable. This paper investi…

cs.CV2021

Self-Supervised Visibility Learning for Novel View Synthesis

Yujiao Shi, Hongdong Li, Xin Yu

We address the problem of novel view synthesis (NVS) from a few sparse source view images. Conventional image-based rendering methods estimate scene geometry and synthesize novel v…

cs.CV20214 cited

ARVo: Learning All-Range Volumetric Correspondence for Video Deblurring

Dongxu Li, Chenchen Xu, Kaihao Zhang +5

Video deblurring models exploit consecutive frames to remove blurs from camera shakes and object motions. In order to utilize neighboring sharp patches, typical methods rely mainly…

eess.IV2021

Modeling the Probabilistic Distribution of Unlabeled Data forOne-shot Medical Image Segmentation

Yuhang Ding, Xin Yu, Yi Yang

Existing image segmentation networks mainly leverage large-scale labeled datasets to attain high accuracy. However, labeling medical images is very expensive since it requires soph…

cs.CV20211 cited

Iterative Optimisation with an Innovation CNN for Pose Refinement

Gerard Kennedy, Zheyu Zhuang, Xin Yu +1

Object pose estimation from a single RGB image is a challenging problem due to variable lighting conditions and viewpoint changes. The most accurate pose estimation networks implem…

cs.CV20205 cited

Uncertainty-Aware Deep Calibrated Salient Object Detection

Jing Zhang, Yuchao Dai, Xin Yu +3

Existing deep neural network based salient object detection (SOD) methods mainly focus on pursuing high network accuracy. However, those methods overlook the gap between network ac…