In this paper, we approach an open problem of artwork identification and propose a new dataset dubbed Open Museum Identification Challenge (Open MIC). It contains photos of exhibits captured in 10 distinct exhibition spaces of several museums which showcase paintings, timepieces, sculptures, glassware, relics, science exhibits, natural history pieces, ceramics, pottery, tools and indigenous crafts. The goal of Open MIC is to stimulate research in domain adaptation, egocentric recognition and few-shot learning by providing a testbed complementary to the famous Office dataset which reaches 90% accuracy. To form our dataset, we captured a number of images per art piece with a mobile phone and wearable cameras to form the source and target data splits, respectively. To achieve robust baselines, we build on a recent approach that aligns per-class scatter matrices of the source and target CNN streams [15]. Moreover, we exploit the positive definite nature of such representations by using end-to-end Bregman divergences and the Riemannian metric. We present baselines such as training/evaluation per exhibition and training/evaluation on the combined set covering 866 exhibit identities. As each exhibition poses distinct challenges e.g., quality of lighting, motion blur, occlusions, clutter, viewpoint and scale variations, rotations, glares, transparency, non-planarity, clipping, we break down results w.r.t. these factors.
Inria（法国国家信息与自动化研究所）；University of Strasbourg（斯特拉斯堡大学）；Technical University of Munich（慕尼黑技术大学）；University of Basel（巴塞尔大学）；Institute of Image-Guided Surgery（图像引导手术研究所）
Unsupervised Domain Adaptation on Person Re-Identification via Dual-level Asymmetric Mutual Learning
Xiamen University（厦门大学）；China University of Mining and Technology（中国矿业大学）；Peng Cheng Laboratory（鹏城实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Tencent（腾讯）
Complementary Pseudo Labels For Unsupervised Domain Adaptation On Person Re-identification
Zhejiang University（浙江大学）；Alibaba-Zhejiang University Joint Institute of Frontier Technologies（阿里巴巴-浙江大学前沿技术联合研究院）；College of Computer Science, Zhejiang University（浙江大学计算机学院）
Domain-Shared Learning and Gradual Alignment for Unsupervised Domain Adaptation Visible-Infrared Person Re-Identification
State Key Laboratory of Electromechanical Integrated Manufacturing of High-Performance Electronic Equipments, Xidian University, Xi’an, Shaanxi 710071, China（高性能电子装备机电一体化制造国家重点实验室，西安电子科技大学，陕西西安710071，中国）；Center for Complex Systems, School of Mechano-Electronic Engineering, Xidian University, Xi’an, Shaanxi 710071, China（复杂系统中心，机电工程学院，西安电子科技大学，陕西西安710071，中国）
人的重新识别（re-ID）旨在检索不同相机拍摄的同一个人的图像。re-ID的一个挑战是当模型用于与训练数据域（源数据）属于不同域的感兴趣数据（目标数据）时的性能保持。无监督领域自适应（UDA）是一个有趣的研究方向，因为它避免了昂贵的目标数据的注释。伪标记方法在基于UDA的re-ID中获得了最好的结果。令人惊讶的是，标记的源数据在该初始化步骤之后被丢弃。然而，我们认为伪标记可以进一步利用标记的源数据，以改善初始化后的训练步骤。为了提高对错误伪标签的鲁棒性，我们提倡在所有训练迭代期间利用标记的源数据和伪标记的目标数据。为了支持我们的指导方针，我们引入了一个框架，它依赖于一个两个分支架构优化分类和三元组损失为基础的度量学习，分别在源和目标域，以允许\emph{adaptability to the target domain}，同时确保\emph{robustness to noisy pseudo-labels}。事实上，共享的低和中级别参数受益于源分类和三重丢失信号，而目标分支的高级别参数学习特定于域的特征。我们的方法很简单，可以很容易地与现有的伪标记UDA方法相结合。我们的实验表明，它是有效的，并提高了性能时，基本方法没有机制来处理伪标签噪声或硬适应任务。我们的方法在常用数据集Market-1501和DukeMTMC-reID上进行评估时达到了最先进的性能，并且在针对更大，更具挑战…
Fish-Vista: A Multi-Purpose Dataset for Understanding & Identification of Traits from Images
Virginia Tech（弗吉尼亚理工大学）；The Ohio State University（俄亥俄州立大学）；Oak Ridge National Laboratory（橡树岭国家实验室）；University of California, Irvine（加州大学欧文分校）；Duke University（杜克大学）；Tulane University（杜兰大学）；Battelle（巴特尔纪念研究所）
Multi-Source Multi-View Graph Domain Adaptation with Hyperbolic Residual Encoding for Cross-Site MDD Identification from rs-fMRI
School of Computer and Artificial Intelligence, Shandong Jianzhu University（山东建筑大学计算机与人工智能学院）；Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China（电子科技大学基础与前沿研究院）；School of Mathematics and Statistics, Chongqing Jiaotong University（重庆交通大学数学与统计学院）；State Key Laboratory of Brain-machine Intelligence, Zhejiang University（浙江大学脑机智能国家重点实验室）；Institute of Computer Vision and Traffic Image Understanding, School of Information Science and Engineering, Chongqing Jiaotong University（重庆交通大学信息科学与工程学院计算机视觉与交通图像理解研究所）；School of Life Sciences, Westlake University（西湖大学生命科学学院）；School of Computer and Artificial Intelligence, Nanjing University of Finance and Economics（南京财经大学计算机与人工智能学院）
Prototype-Guided Continual Adaptation for Class-Incremental Unsupervised Domain Adaptation
Peking University（北京大学）；South China University of Technology（华南理工大学）；National University of Singapore（新加坡国立大学）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）
Insect Identification in the Wild: The AMI Dataset
Mila - Quebec AI Institute（米拉-魁北克人工智能研究所）；Aarhus University（奥胡斯大学）；Vermont Center for Ecostudies（佛蒙特生态研究中心）；UK Centre for Ecology & Hydrology（英国生态与水文中心）；Centre de données sur le patrimoine naturel du Québec（魁北克自然遗产数据中心）；Smithsonian Tropical Research Institute（史密森尼热带研究所）；Université de Sherbrooke（舍布鲁克大学）；Montreal Insectarium（蒙特利尔昆虫馆）
DeepMerge II: Building Robust Deep Learning Algorithms for Merging Galaxy Identification Across Domains
Fermi National Accelerator Laboratory（费米国家加速器实验室）；University of Chicago（芝加哥大学）；Argonne National Laboratory（阿贡国家实验室）；Oak Ridge National Laboratory（橡树岭国家实验室）；Space Telescope Science Institute（太空望远镜科学研究所）；Kavli Institute for Cosmological Physics（卡夫利宇宙物理研究所）
Adaptation and Re-Identification Network: An Unsupervised Deep Transfer Learning Approach to Person Re-Identification
Graduate Institute of Communication Engineering, National Taiwan University（台湾大学通信工程研究所）；Department of Electrical Engineering, National Taiwan University（台湾大学电机工程学系）；Umbo Computer Vision（Umbo计算机视觉（公司））
Cross-Dataset Person Re-Identification via Unsupervised Pose Disentanglement and Adaptation
National Taiwan University（台湾大学）；MOST Joint Research Center for AI Technology and All Vista Healthcare（科技部AI技术与全视医疗照护联合研究中心）；ASUS Intelligent Cloud Services（华硕智能云服务）
Attentive WaveBlock: Complementarity-enhanced Mutual Networks for Unsupervised Domain Adaptation in Person Re-identification and Beyond
Beihang University（北京航空航天大学）；University of Technology Sydney（悉尼科技大学）；IIAI（起源人工智能研究院）；Tencent AI Lab（腾讯人工智能实验室）；Inception Institute of Artificial Intelligence（起源人工智能研究院）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Dual-Stream Reciprocal Disentanglement Learning for Domain Adaptation Person Re-Identification
Kunming University of Science and Technology（昆明理工大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；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）
Learning to Adapt Invariance in Memory for Person Re-identification
Department of Artificial Intelligence, Xiamen University, Xiamen 361005, China（中国厦门大学人工智能系，厦门 361005）；Centre for Artificial Intelligence, University of Technology Sydney, Ultimo, NSW 2007, Australia（澳大利亚悉尼科技大学人工智能中心，新南威尔士州 2007）；Research School of Computer Science, The Australian National University, Canberra, ACT 0200, Australia（澳大利亚国立大学计算机科学研究学院，澳大利亚首都领地 0200）；Postdoc Center of Information and Communication Engineering, Xiamen University, Xiamen 361005, China（中国厦门大学信息与通信工程博士后流动站，厦门 361005）
