This paper revisits the principle of uniform convergence in statistical learning, discusses how it acts as the foundation behind machine learning, and attempts to gain a better understanding of the essential problem that current deep learning algorithms are solving. Using computer vision as an example domain in machine learning, the discussion shows that recent research trends in leveraging increasingly large-scale data to perform pre-training for representation learning are largely to reduce the discrepancy between a practically tractable empirical loss and its ultimately desired but intractable expected loss. Furthermore, this paper suggests a few future research directions, predicts the continued increase of data, and argues that more fundamental research is needed on robustness, interpretability, and reasoning capabilities of machine learning by incorporating structure and knowledge.
Cross-Domain Foundation Model Adaptation: Pioneering Computer Vision Models for Geophysical Data Analysis
School of Earth and Space Sciences, University of Science and Technology of China（中国科学技术大学地球和空间科学学院）；Microsoft Applied Sciences Group（微软应用科学组）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）
Florence: A New Foundation Model for Computer Vision
Microsoft Cloud and AI（微软云与人工智能部门）；Microsoft Research Redmond（微软雷德蒙德研究院）
对我们多样化和开放世界的自动视觉理解要求计算机视觉模型能够很好地泛化，并对特定任务进行最小化的定制，类似于人类视觉。计算机视觉基础模型是在各种大规模数据集上训练的，可以适应广泛的下游任务，对于解决现实世界的计算机视觉应用至关重要。虽然现有的视觉基础模型，如CLIP，ALIGN和Wu Dao 2.0主要集中在将图像和文本表示映射到跨模态共享表示，但我们引入了一个新的计算机视觉基础模型Florence，将表示从粗（场景）扩展到细（对象），从静态（图像）扩展到动态（视频），从RGB扩展到多模态（标题，深度）。通过将通用的视觉语言表示从Web规模的图像-文本数据，我们的佛罗伦萨模型可以很容易地适应各种计算机视觉任务，如分类，检索，目标检测，VQA，图像字幕，视频检索和动作识别。此外，Florence在许多类型的迁移学习中表现出色：完全采样微调，线性探测，Few-Shot迁移和新颖图像和对象的zero-shot迁移。所有这些属性对于我们的视觉基础模型服务于通用视觉任务来说都是至关重要的。Florence在44个代表性基准中的大多数中取得了新的最先进的结果，例如，ImageNet-1 K zero-shot分类，top-1精度为83.74，top-5精度为97.18，COCO微调为62.4 mAP，VQA为80.36，Kinetics-600为87.8。
Scaling up self-supervised learning for improved surgical foundation models
Department of Electrical Engineering, Video Coding \& Architectures, Eindhoven University of Technology, Eindhoven, The Netherlands；Department of Biomedical Engineering, Medical Image Analysis, Eindhoven University of Technology, Eindhoven, The Netherlands；Department of Surgery, University Medical Center Utrecht, Utrecht, The Netherlands；Department of Oncological Urology, University Medical Center Utrecht, Utrecht, The Netherlands；Department of Urology, Catharina Hospital, Eindhoven, The Netherlands
XLANG Lab, The University of Hong Kong（香港大学XLANG实验室）；Moonshot AI；Stanford University（斯坦福大学）；University of Waterloo（滑铁卢大学）；Carnegie Mellon University（卡内基梅隆大学）
Learning to Adapt Foundation Model DINOv2 for Capsule Endoscopy Diagnosis
The Chinese University of Hong Kong（香港中文大学）；Shenzhen Research Institute, The Chinese University of Hong Kong（香港中文大学深圳研究院）；University of Science and Technology Beijing（北京科技大学）；City University of Hong Kong（香港城市大学）；National University of Singapore（新加坡国立大学）
Lost in Distortion: Uncovering the Domain Gap Between Computer Vision and Brain Imaging -- A Study on Pretraining for Age Prediction
Center for Translational Research in Neuroimaging and Data Science（转化神经影像与数据科学研究中心）；College of Artificial Intelligence, Chengdu University of Information Technology（成都信息科技大学人工智能学院）；Department of Applied Mathematics and Statistics（应用数学与统计学系）；McWilliams School of Biomedical Informatics（麦卡威廉生物医学信息学学院）；Department of Applied Mathematics and Theoretical Physics（应用数学与理论物理系）；School of Biomedical Engineering and Imaging Sciences（生物医学工程与影像科学学院）
Bosch Center for AI（博世人工智能中心）；University of Tübingen（蒂宾根大学）；University of Mannheim（曼海姆大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Bosch Center for Artificial Intelligence（博世人工智能中心）
UltraBench 2: Towards Robust Evaluation of Vision Foundation Models on Ultrasound
University of California, Los Angeles（加州大学洛杉矶分校）；Université Laval（拉瓦尔大学）；Mila – Quebec AI Institute（米拉-魁北克人工智能研究所）；UCLA David Geffen School of Medicine（加州大学洛杉矶分校大卫·格芬医学院）
YOLO with Kolmogorov-Arnold networks and vision-language foundation models for interpretable object detection with trustworthy multimodal AI in computer vision perception
University of Bath（巴斯大学）；Zhejiang University（浙江大学）
An Investigation of Visual Foundation Models Robustness
Centre for Secure Information Technologies (CSIT)（安全信息科技中心）；Queen’s University Belfast（贝尔法斯特女王大学）；Department of Information Engineering and Computer Science（信息工程与计算机科学系）；University of Trento（特伦托大学）
Method for Evaluating the Number of Signal Sources and Application to Non-invasive Brain-computer Interface
Institute of Artificial Intelligence（人工智能研究所）；M.V.Lomonosov Moscow State University（罗蒙诺索夫莫斯科国立大学）；Rebis LLC（Rebis有限责任公司）；Aicumene LLC（Aicumene有限责任公司）；Neurosputnik LLC（Neurosputnik有限责任公司）
Towards the Unification of Generative and Discriminative Visual Foundation Model: A Survey
University of California Los Angeles（加利福尼亚大学洛杉矶分校）；Rice University（莱斯大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Michigan（密歇根大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Walmart Global Tech（沃尔玛全球技术公司）；Santa Clara University（圣克拉拉大学）；Shanghai AI Laboratory（上海人工智能实验室）；Johns Hopkins University（约翰斯·霍普金斯大学）
