117 citations · 816 across the 78 of their papers we have counts for
20 papers · 1 filter
Which Pixel to Annotate: a Label-Efficient Nuclei Segmentation Framework
Wei Lou, Haofeng Li, Guanbin Li +2
Recently deep neural networks, which require a large amount of annotated samples, have been widely applied in nuclei instance segmentation of H\&E stained pathology images. However…
MM-3DScene: 3D Scene Understanding by Customizing Masked Modeling with Informative-Preserved Reconstruction and Self-Distilled Consistency
Mingye Xu, Mutian Xu, Tong He +4
Masked Modeling (MM) has demonstrated widespread success in various vision challenges, by reconstructing masked visual patches. Yet, applying MM for large-scale 3D scenes remains a…
MIMO Is All You Need : A Strong Multi-In-Multi-Out Baseline for Video Prediction
Shuliang Ning, Mengcheng Lan, Yanran Li +5
The mainstream of the existing approaches for video prediction builds up their models based on a Single-In-Single-Out (SISO) architecture, which takes the current frame as input to…
Learning 3D Scene Priors with 2D Supervision
Yinyu Nie, Angela Dai, Xiaoguang Han +1
Holistic 3D scene understanding entails estimation of both layout configuration and object geometry in a 3D environment. Recent works have shown advances in 3D scene estimation fro…
Mutual Guidance and Residual Integration for Image Enhancement
Kun Zhou, KenKun Liu, Wenbo Li +2
Previous studies show the necessity of global and local adjustment for image enhancement. However, existing convolutional neural networks (CNNs) and transformer-based models face g…
Point Cloud Scene Completion with Joint Color and Semantic Estimation from Single RGB-D Image
Zhaoxuan Zhang, Xiaoguang Han, Bo Dong +3
We present a deep reinforcement learning method of progressive view inpainting for colored semantic point cloud scene completion under volume guidance, achieving high-quality scene…
A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective
Chaoqi Chen, Yushuang Wu, Qiyuan Dai +5
Graph Neural Networks (GNNs) have gained momentum in graph representation learning and boosted the state of the art in a variety of areas, such as data mining (\emph{e.g.,} social…
Towards High-Fidelity Single-view Holistic Reconstruction of Indoor Scenes
Haolin Liu, Yujian Zheng, Guanying Chen +2
We present a new framework to reconstruct holistic 3D indoor scenes including both room background and indoor objects from single-view images. Existing methods can only produce 3D…
PIFu for the Real World: A Self-supervised Framework to Reconstruct Dressed Human from Single-view Images
Zhangyang Xiong, Dong Du, Yushuang Wu +4
It is very challenging to accurately reconstruct sophisticated human geometry caused by various poses and garments from a single image. Recently, works based on pixel-aligned impli…
Multi-level Consistency Learning for Semi-supervised Domain Adaptation
Zizheng Yan, Yushuang Wu, Guanbin Li +3
Semi-supervised domain adaptation (SSDA) aims to apply knowledge learned from a fully labeled source domain to a scarcely labeled target domain. In this paper, we propose a Multi-l…
Relation Matters: Foreground-aware Graph-based Relational Reasoning for Domain Adaptive Object Detection
Chaoqi Chen, Jiongcheng Li, Hong-Yu Zhou +4
Domain Adaptive Object Detection (DAOD) focuses on improving the generalization ability of object detectors via knowledge transfer. Recent advances in DAOD strive to change the emp…
DArch: Dental Arch Prior-assisted 3D Tooth Instance Segmentation
Liangdong Qiu, Chongjie Ye, Pei Chen +3
Automatic tooth instance segmentation on 3D dental models is a fundamental task for computer-aided orthodontic treatments. Existing learning-based methods rely heavily on expensive…
Registering Explicit to Implicit: Towards High-Fidelity Garment mesh Reconstruction from Single Images
Heming Zhu, Lingteng Qiu, Yuda Qiu +1
Fueled by the power of deep learning techniques and implicit shape learning, recent advances in single-image human digitalization have reached unprecedented accuracy and could reco…
SharpContour: A Contour-based Boundary Refinement Approach for Efficient and Accurate Instance Segmentation
Chenming Zhu, Xuanye Zhang, Yanran Li +3
Excellent performance has been achieved on instance segmentation but the quality on the boundary area remains unsatisfactory, which leads to a rising attention on boundary refineme…
Compound Domain Generalization via Meta-Knowledge Encoding
Chaoqi Chen, Jiongcheng Li, Xiaoguang Han +2
Domain generalization (DG) aims to improve the generalization performance for an unseen target domain by using the knowledge of multiple seen source domains. Mainstream DG methods…
Exploring Motion Ambiguity and Alignment for High-Quality Video Frame Interpolation
Kun Zhou, Wenbo Li, Xiaoguang Han +1
For video frame interpolation (VFI), existing deep-learning-based approaches strongly rely on the ground-truth (GT) intermediate frames, which sometimes ignore the non-unique natur…
TO-Scene: A Large-scale Dataset for Understanding 3D Tabletop Scenes
Mutian Xu, Pei Chen, Haolin Liu +1
Many basic indoor activities such as eating or writing are always conducted upon different tabletops (e.g., coffee tables, writing desks). It is indispensable to understanding tabl…
PVSeRF: Joint Pixel-, Voxel- and Surface-Aligned Radiance Field for Single-Image Novel View Synthesis
Xianggang Yu, Jiapeng Tang, Yipeng Qin +4
We present PVSeRF, a learning framework that reconstructs neural radiance fields from single-view RGB images, for novel view synthesis. Previous solutions, such as pixelNeRF, rely…
Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution Priors
Chaofeng Chen, Xinyu Shi, Yipeng Qin +4
A key challenge of real-world image super-resolution (SR) is to recover the missing details in low-resolution (LR) images with complex unknown degradations (e.g., downsampling, noi…
PointMatch: A Consistency Training Framework for Weakly Supervised Semantic Segmentation of 3D Point Clouds
Yushuang Wu, Zizheng Yan, Shengcai Cai +4
Semantic segmentation of point cloud usually relies on dense annotation that is exhausting and costly, so it attracts wide attention to investigate solutions for the weakly supervi…