most citedExplore Contextual Information for 3D Scene Graph Generation

1 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2022

Object Detection in Foggy Scenes by Embedding Depth and Reconstruction into Domain Adaptation

Xin Yang, Michael Bi Mi, Yuan Yuan +2

Most existing domain adaptation (DA) methods align the features based on the domain feature distributions and ignore aspects related to fog, background and target objects, renderin…

cs.CV20221 cited

Explore Contextual Information for 3D Scene Graph Generation

Yuanyuan Liu, Chengjiang Long, Zhaoxuan Zhang +4

3D scene graph generation (SGG) has been of high interest in computer vision. Although the accuracy of 3D SGG on coarse classification and single relation label has been gradually…

cs.CV2022

Wider and Higher: Intensive Integration and Global Foreground Perception for Image Matting

Yu Qiao, Ziqi Wei, Yuhao Liu +4

This paper reviews recent deep-learning-based matting research and conceives our wider and higher motivation for image matting. Many approaches achieve alpha mattes with complex en…

cs.CV20221 cited

Hierarchical and Progressive Image Matting

Yu Qiao, Yuhao Liu, Ziqi Wei +4

Most matting researches resort to advanced semantics to achieve high-quality alpha mattes, and direct low-level features combination is usually explored to complement alpha details…

eess.IV2021

SECP-Net: SE-Connection Pyramid Network of Organ At Risk Segmentation for Nasopharyngeal Carcinoma

Zexi Huang, Lihua Guo, Xin Yang +1

Nasopharyngeal carcinoma (NPC) is a kind of malignant tumor. Accurate and automatic segmentation of organs at risk (OAR) of computed tomography (CT) images is clinically significan…