PointMoment:Mixed-Moment-based Self-Supervised Representation Learning for 3D Point Clouds
School of Information Science and Technology, Northwest University（西北大学信息科学与技术学院）；National and Local Joint Engineering Research Center for Cultural Heritage Digitization（文化遗产数字化国家地方联合工程研究中心）
Xin Cao, Xinxin Han, Yifan Wang, Mengna Yang, Kang Li
Large and rich data is a prerequisite for effective training of deep neural networks. However, the irregularity of point cloud data makes manual annotation time-consuming and laborious. Self-supervised representation learning, which leverages the intrinsic structure of large-scale unlabelled data to learn meaningful feature representations, has attracted increasing attention in the field of point cloud research. However, self-supervised representation learning often suffers from model collapse, resulting in reduced information and diversity of the learned representation, and consequently degrading the performance of downstream tasks. To address this problem, we propose PointMoment, a novel framework for point cloud self-supervised representation learning that utilizes a high-order mixed moment loss function rather than the conventional contrastive loss function. Moreover, our framework does not require any special techniques such as asymmetric network architectures, gradient stopping, etc. Specifically, we calculate the high-order mixed moment of the feature variables and force them to decompose into products of their individual moment, thereby making multiple variables more independent and minimizing the feature redundancy. We also incorporate a contrastive learning approach to maximize the feature invariance under different data augmentations of the same point cloud. Experimental results show that our approach outperforms previous unsupervised learning methods on the downstream task of 3D point cloud classification and segmentation.
Domain-invariant Mixed-domain Semi-supervised Medical Image Segmentation with Clustered Maximum Mean Discrepancy Alignment
Carnegie Mellon University（卡内基梅隆大学）；Ho Chi Minh University of Science（胡志明科学大学）；Northwestern University（西北大学）；University of North Carolina at Charlotte（北卡罗来纳州夏洛特大学）
Self-supervised Deep Reconstruction of Mixed Strip-shredded Text Documents
Instituto Federal do Espírito Santo (IFES)（埃斯皮里图桑托联邦学院）；Universidade Federal do Espírito Santo (UFES)（埃斯皮里图桑托联邦大学）；École de Technologie Supérieure (ÉTS)（高等技术学院）；Université du Québec（魁北克大学）
Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation
State Key Laboratory for Novel Software Technology, Nanjing University（新型软件技术国家重点实验室，南京大学）；National Institute of Healthcare Data Science, Nanjing University（健康数据科学国家研究院，南京大学）；Nanjing Drum Tower Hospital, Nanjing, Jiangsu, China（南京鼓楼医院，南京，江苏，中国）
Mixed Supervised Object Detection with Robust Objectness Transfer
Chinese Academy of Sciences (CASIA)（中国科学院（CASIA））；Center for Research on Intelligent Perception and Computing (CRIPAC)（智能感知与计算研究中心（CRIPAC））；National Laboratory of Pattern Recognition (NLPR)（模式识别国家重点实验室（NLPR））；Institute of Automation（自动化研究所）；University of Chinese Academy of Sciences (UCAS)（中国科学院大学（UCAS））；CAS Center for Excellence in Brain Science and Intelligence Technology（中国科学院脑科学与智能技术卓越创新中心）；University of Dundee（邓迪大学）；School of Science and Engineering（理工学院）；Computing（计算机学院）
A Simple Data Mixing Prior for Improving Self-Supervised Learning
South China University of Technology（华南理工大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Massachusetts Institute of Technology（麻省理工学院）；UC Santa Cruz（加州大学圣克鲁兹分校）
数据混合（例如，Mixup、Cutmix、ResizeMix）是推进识别模型的重要组件。在本文中，我们重点研究其有效性的自我监督设置。通过注意到共享相同源图像的混合图像本质上彼此相关，我们在此提出了SSTK，简称$\textbf{S}$ imple $\textbf{D}$ ata $\textbf{M}$ ixing $\textbf{P}$ rior，以捕获这种简单而重要的先验，并将这种混合图像定位为附加的$\textbf{positive pairs}$，以促进自监督表示学习。我们的实验验证了所提出的SSTOM使得数据混合能够帮助一组自监督学习框架（例如，MoCo）实现更好的准确性和分布外鲁棒性。更值得注意的是，我们的SDMP是第一种成功利用数据混合来提高（而不是损害）自监督环境中视觉Transformer性能的方法。代码可在https://github.com/OliverRensu/SDMP上公开获取
Steady Progress Beats Stagnation: Mutual Aid of Foundation and Conventional Models in Mixed Domain Semi-Supervised Medical Image Segmentation
Nanjing University（南京大学）；Southeast University（东南大学）；State Key Laboratory for Novel Software Technology（计算机软件新技术国家重点实验室）；National Institute of Healthcare Data Science（国家医疗数据科学研究院）
Surgical Action Triplet Detection by Mixed Supervised Learning of Instrument-Tissue Interactions
ICube Laboratory（ICube实验室）；University of Strasbourg（斯特拉斯堡大学）；IHU Strasbourg（斯特拉斯堡大学医院研究所）；University Hospital of Strasbourg（斯特拉斯堡大学医院）；CNRS（法国国家科学研究中心）
Mixed-UNet: Refined Class Activation Mapping for Weakly-Supervised Semantic Segmentation with Multi-scale Inference
Shandong University（山东大学）；Institute of Tsinghua-Berkeley Shenzhen, Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院清华-伯克利深圳学院）；Hangzhou Dianzi University（杭州电子科技大学）；Shenzhen ZNV Technology Co., Ltd（深圳中兴网信科技有限公司）；Shenzhen Graduate School of Harbin Institute of Technology（哈尔滨工业大学深圳研究生院）；Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院生物医药与健康工程研究院）
Semi-supervised Domain Adaptation based on Dual-level Domain Mixing for Semantic Segmentation
Dalian University of Technology（大连理工大学）；Data Storage and Intelligent Vision Technical Research Dept Huawei Cloud（华为云数据存储与智能视觉技术研究部）；Key Lab of Machine Perception Peking University（北京大学机器感知重点实验室）
Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentation
Nanjing University（南京大学）；Southeast University（东南大学）；Shandong Women’s University（山东女子学院）；State Key Laboratory for Novel Software Technology（现代软件技术国家重点实验室）；National Institute of Healthcare Data Science（国家医疗数据科学研究院）
Joint Semi-supervised 3D Super-Resolution and Segmentation with Mixed Adversarial Gaussian Domain Adaptation
MRC London Institute of Medical Sciences（MRC伦敦医学科学研究所）；Imperial College London（帝国理工学院）；National Heart Research Institute Singapore（新加坡国家心脏研究所）；National Heart Center Singapore（新加坡国家心脏中心）；Technical University of Munich（慕尼黑工业大学）
Background-Mixed Augmentation for Weakly Supervised Change Detection
School of Computer Science and Technology, Civil Aviation University of China（中国民航大学计算机科学与技术学院）；Agency for Science, Technology and Research（新加坡科技研究局）；Zhejiang Sci-Tech University（浙江理工大学）；Nanyang Technological University（南洋理工大学）
Weakly Supervised Lymph Nodes Segmentation Based on Partial Instance Annotations with Pre-trained Dual-branch Network and Pseudo Label Learning
University of Electronic Science and Technology of China（电子科技大学）；Shanghai AI Laboratory（上海人工智能实验室）；Sichuan Cancer Hospital & Institute, Sichuan Cancer Center（四川省肿瘤医院·研究所（四川省癌症防治中心））
GuidedMix-Net: Learning to Improve Pseudo Masks Using Labeled Images as Reference
Southern University of Science and Technology（南方科技大学）；Xiamen University（厦门大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Inception Institute of Artificial Intelligence（因赛普雄人工智能研究院）
GuidedMix-Net: Semi-supervised Semantic Segmentation by Using Labeled Images as Reference
Shenzhen Microbt Electronics Technology Co., Ltd（深圳比特微电子科技有限公司）；Tencent Jarvis Lab（腾讯贾维斯实验室）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Xiamen University（厦门大学）；National Center for Artificial Intelligence, Saudi Data and Artificial Intelligence Authority（沙特数据和人工智能管理局国家人工智能中心）
