In computational pathology, we often face a scarcity of annotations and a large amount of unlabeled data. One method for dealing with this is semi-supervised learning which is commonly split into a self-supervised pretext task and a subsequent model fine-tuning. Here, we compress this two-stage training into one by introducing S5CL, a unified framework for fully-supervised, self-supervised, and semi-supervised learning. With three contrastive losses defined for labeled, unlabeled, and pseudo-labeled images, S5CL can learn feature representations that reflect the hierarchy of distance relationships: similar images and augmentations are embedded the closest, followed by different looking images of the same class, while images from separate classes have the largest distance. Moreover, S5CL allows us to flexibly combine these losses to adapt to different scenarios. Evaluations of our framework on two public histopathological datasets show strong improvements in the case of sparse labels: for a H&E-stained colorectal cancer dataset, the accuracy increases by up to 9% compared to supervised cross-entropy loss; for a highly imbalanced dataset of single white blood cells from leukemia patient blood smears, the F1-score increases by up to 6%.
Unifying Visual and Vision-Language Tracking via Contrastive Learning
Deep Space Exploration Laboratory（深空探测实验室）；University of Science and Technology of China（中国科学技术大学）；Intelligent Science Technology Academy of CASIC（中国航天科工集团智能科学技术研究院）
Restoring Vision in Hazy Weather with Hierarchical Contrastive Learning
Nanjing University（南京大学）；Memorial University of Newfoundland（纽芬兰纪念大学）；Australian National University（澳大利亚国立大学）；Sun Yat-sen University（中山大学）；Wenzhou University（温州大学）
Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detection
Zhejiang University（浙江大学）；JD Explore Academy（京东探索研究院）；National University of Singapore（新加坡国立大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Balanced Hierarchical Contrastive Learning with Decoupled Queries for Fine-grained Object Detection in Remote Sensing Images
School of Computer Science and Engineering, Nanjing University of Science and Technology, China（南京理工大学计算机科学与工程学院）；College of Computer Science, Zhejiang University, China（浙江大学计算机学院）
基于实例判别的自监督学习已经取得了显著的进展。特别是，对比学习，它把每个图像以及它的增强作为一个单独的类，并试图区分他们从所有其他图像，已被证明是有效的表示学习。然而，推开两个事实上相似的图像对于一般表示来说是次优的。本文提出了一种层次化的语义对齐策略，将单个图像生成的视图扩展为\textbf{Cross-samples and Multi-level}表示，并以层次化的方式对语义相似图像的不变性进行建模。这是通过扩展对比度损失来实现的，以允许每个锚点有多个阳性，并在网络的不同层显式地将语义相似的图像/补丁拉到一起。我们的方法，被称为CsMl，有能力整合多层次的视觉表示在一个强大的方式在整个样本。CsMl适用于当前基于对比学习的方法，并且持续地提高性能。值得注意的是，使用moco作为实例，CsMl实现了\textbf{76.6\% } top-1准确度，其中使用ResNet-50作为骨架进行线性评估，并且分别仅具有1\%和10\%标记的\textbf{66.7\%}和\textbf{75.1\%} top-1准确度。\textbf{All these numbers set the new state-of-the-art.}
TreeGaussian: Tree-Guided Cascaded Contrastive Learning for Hierarchical Consistent 3D Gaussian Scene Segmentation and Understanding
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Beihang University（北京航空航天大学）
HiCo: Hierarchical Contrastive Learning for Ultrasound Video Model Pretraining
Shanghai Jiaotong University（上海交通大学）；CloudWalk Technology Co., Ltd（云从科技有限公司）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）
Prior-guided Hierarchical Instance-pixel Contrastive Learning for Ultrasound Speckle Noise Suppression
School of Biological Science and Medical Engineering, Southeast University（生物科学与医学工程学院，东南大学）；Department of Biomedical Engineering, The Ohio State University（生物医学工程系，奥斯特大学）；Jiangsu Key Laboratory of Biomaterials and Devices, Southeast University（江苏省生物材料与器件重点实验室，东南大学）；State Key Laboratory of Digital Medical Engineering, Southeast University（国家数字医学工程重点实验室，东南大学）
UniH$^3$: Unifying Hierarchical Homogeneity and Heterogeneity for All-in-One Medical Image Restoration
School of Biological Science and Medical Engineering, Beihang University（北京航空航天大学生物科学与医学工程学院）；Department of Biomedical Engineering, Tsinghua University（清华大学生物医学工程系）
H3Former: Hypergraph-based Semantic-Aware Aggregation via Hyperbolic Hierarchical Contrastive Loss for Fine-Grained Visual Classification
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；College of Software, Jilin University（吉林大学软件学院）；School of Software, Tsinghua University（清华大学软件学院）
SIGMMA: Hierarchical Graph-Based Multi-Scale Multi-modal Contrastive Alignment of Histopathology Image and Spatial Transcriptome
Wellcome Sanger Institute（沃森桑格研究所）；Cambridge Centre for AI in Medicine（剑桥人工智能医学中心）；Institute of AI for Health（人工智能与健康研究所）；Cambridge Stem Cell Institute（剑桥干细胞研究所）
Hierarchical Consensus Network for Multiview Feature Learning
School of Information Science and Engineering, Lanzhou University（兰州大学信息科学与工程学院）；Australian Artificial Intelligence Institute, University of Technology Sydney（悉尼科技大学澳大利亚人工智能学院）
Transformed Multi-view 3D Shape Features with Contrastive Learning
Instituto de Ciências Matemáticas e de Computação (ICMC), Universidade de São Paulo (USP), São Carlos, SP, Brazil（数学与计算机科学研究所（ICMC），圣保罗大学（USP），圣卡洛斯，SP，巴西）
HSCR: Hierarchical Self-Contrastive Rewarding for Aligning Medical Vision Language Models
Zhejiang University（浙江大学）；Byte Dance（字节跳动）；National University of Singapore（新加坡国立大学）；Angelalign Inc China（Angelalign中国公司）；Zhejiang Key Laboratory of Medical Imaging Artificial Intelligence（浙江省医学影像人工智能重点实验室）
HiCMAE: Hierarchical Contrastive Masked Autoencoder for Self-Supervised Audio-Visual Emotion Recognition
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Department of Automation, Tsinghua University（清华大学自动化系）；Beijing National Research Center for Information Science and Technology, Tsinghua University（清华大学北京信息科学与技术国家研究中心）
BioCLIP 2: Emergent Properties from Scaling Hierarchical Contrastive Learning
The Ohio State University（俄亥俄州立大学）；Smithsonian Institution（史密森学会）；UNC Chapel Hill（北卡罗来纳大学教堂山分校）；University of California, Irvine（加州大学伊市分校）；Princeton University（普林斯顿大学）；Duke University（杜克大学）
Emergent Hierarchical Monosemantic Neurons from the Group-Contrastive Forward-Forward Algorithm
National University of Singapore（新加坡国立大学）；Lund University（隆德大学）；Chinese University of Hong Kong（香港中文大学）；Indian Institute of Technology, Dhanbad（印度理工学院（丹巴德分校））
UINO-FSS: Unifying Representation Learning and Few-shot Segmentation via Hierarchical Distillation and Mamba-HyperCorrelation
School of Artificial Intelligence and the National Engineering Laboratory of Big Data System Computing Technology, Shenzhen University（人工智能学院和大数据系统计算技术国家工程实验室，深圳大学）；Guangdong Provincial Key Laboratory of Intelligent Information Processing, China（广东省智能信息处理重点实验室，中国）；School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University（生物医学工程学院，深圳大学医学院，深圳大学）；Department of Computer Science, University of Nottingham Ningbo China（计算机科学系，宁波大学中国）
