416 citations · 2.7k across the 106 of their papers we have counts for
83 papers · 1 filter
SketchMetaFace: A Learning-based Sketching Interface for High-fidelity 3D Character Face Modeling
Zhongjin Luo, Dong Du, Heming Zhu +3
Modeling 3D avatars benefits various application scenarios such as AR/VR, gaming, and filming. Character faces contribute significant diversity and vividity as a vital component of…
Exploration and Exploitation of Unlabeled Data for Open-Set Semi-Supervised Learning
Ganlong Zhao, Guanbin Li, Yipeng Qin +5
In this paper, we address a complex but practical scenario in semi-supervised learning (SSL) named open-set SSL, where unlabeled data contain both in-distribution (ID) and out-of-d…
EGC: Image Generation and Classification via a Diffusion Energy-Based Model
Qiushan Guo, Chuofan Ma, Yi Jiang +3
Learning image classification and image generation using the same set of network parameters is a challenging problem. Recent advanced approaches perform well in one task often exhi…
Multi-Level Contrastive Learning for Dense Prediction Task
Qiushan Guo, Yizhou Yu, Yi Jiang +3
In this work, we present Multi-Level Contrastive Learning for Dense Prediction Task (MCL), an efficient self-supervised method for learning region-level feature representation for…
Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation
Ruifei Zhang, Sishuo Liu, Yizhou Yu +1
Biomedical image segmentation plays a significant role in computer-aided diagnosis. However, existing CNN based methods rely heavily on massive manual annotations, which are very e…
Adaptive Context Selection for Polyp Segmentation
Ruifei Zhang, Guanbin Li, Zhen Li +3
Accurate polyp segmentation is of great significance for the diagnosis and treatment of colorectal cancer. However, it has always been very challenging due to the diverse shape and…
GraVIS: Grouping Augmented Views from Independent Sources for Dermatology Analysis
Hong-Yu Zhou, Chixiang Lu, Liansheng Wang +1
Self-supervised representation learning has been extremely successful in medical image analysis, as it requires no human annotations to provide transferable representations for dow…
I2F: A Unified Image-to-Feature Approach for Domain Adaptive Semantic Segmentation
Haoyu Ma, Xiangru Lin, Yizhou Yu
Unsupervised domain adaptation (UDA) for semantic segmentation is a promising task freeing people from heavy annotation work. However, domain discrepancies in low-level image stati…
PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis
Hong-Yu Zhou, Chixiang Lu, Chaoqi Chen +2
Recent advances in self-supervised learning (SSL) in computer vision are primarily comparative, whose goal is to preserve invariant and discriminative semantics in latent represent…
Geometry-Aware Network for Domain Adaptive Semantic Segmentation
Yinghong Liao, Wending Zhou, Xu Yan +3
Measuring and alleviating the discrepancies between the synthetic (source) and real scene (target) data is the core issue for domain adaptive semantic segmentation. Though recent w…
RankDNN: Learning to Rank for Few-shot Learning
Qianyu Guo, Hongtong Gong, Xujun Wei +4
This paper introduces a new few-shot learning pipeline that casts relevance ranking for image retrieval as binary ranking relation classification. In comparison to image classifica…
Mix and Reason: Reasoning over Semantic Topology with Data Mixing for Domain Generalization
Chaoqi Chen, Luyao Tang, Feng Liu +3
Domain generalization (DG) enables generalizing a learning machine from multiple seen source domains to an unseen target one. The general objective of DG methods is to learn semant…
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…
Neighborhood Collective Estimation for Noisy Label Identification and Correction
Jichang Li, Guanbin Li, Feng Liu +1
Learning with noisy labels (LNL) aims at designing strategies to improve model performance and generalization by mitigating the effects of model overfitting to noisy labels. The ke…
One-Shot Medical Landmark Localization by Edge-Guided Transform and Noisy Landmark Refinement
Zihao Yin, Ping Gong, Chunyu Wang +2
As an important upstream task for many medical applications, supervised landmark localization still requires non-negligible annotation costs to achieve desirable performance. Besid…
Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy Labels
Ganlong Zhao, Guanbin Li, Yipeng Qin +2
Deep models trained with noisy labels are prone to over-fitting and struggle in generalization. Most existing solutions are based on an ideal assumption that the label noise is cla…
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…
Scale-Equivalent Distillation for Semi-Supervised Object Detection
Qiushan Guo, Yao Mu, Jianyu Chen +3
Recent Semi-Supervised Object Detection (SS-OD) methods are mainly based on self-training, i.e., generating hard pseudo-labels by a teacher model on unlabeled data as supervisory s…
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…
Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learning
Yangji He, Weihan Liang, Dongyang Zhao +4
This paper presents new hierarchically cascaded transformers that can improve data efficiency through attribute surrogates learning and spectral tokens pooling. Vision transformers…
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…
BOAT: Bilateral Local Attention Vision Transformer
Tan Yu, Gangming Zhao, Ping Li +1
Vision Transformers achieved outstanding performance in many computer vision tasks. Early Vision Transformers such as ViT and DeiT adopt global self-attention, which is computation…
Context-LGM: Leveraging Object-Context Relation for Context-Aware Object Recognition
Mingzhou Liu, Xinwei Sun, Fandong Zhang +2
Context, as referred to situational factors related to the object of interest, can help infer the object's states or properties in visual recognition. As such contextual features a…
Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contexts
Hong-Yu Zhou, Chixiang Lu, Sibei Yang +2
Preserving maximal information is one of principles of designing self-supervised learning methodologies. To reach this goal, contrastive learning adopts an implicit way which is co…