The goal of metric learning is to learn a function that maps samples to a lower-dimensional space where similar samples lie closer than dissimilar ones. Particularly, deep metric learning utilizes neural networks to learn such a mapping. Most approaches rely on losses that only take the relations between pairs or triplets of samples into account, which either belong to the same class or two different classes. However, these methods do not explore the embedding space in its entirety. To this end, we propose an approach based on message passing networks that takes all the relations in a mini-batch into account. We refine embedding vectors by exchanging messages among all samples in a given batch allowing the training process to be aware of its overall structure. Since not all samples are equally important to predict a decision boundary, we use an attention mechanism during message passing to allow samples to weigh the importance of each neighbor accordingly. We achieve state-of-the-art results on clustering and image retrieval on the CUB-200-2011, Cars196, Stanford Online Products, and In-Shop Clothes datasets. To facilitate further research, we make available the code and the models at https://github.com/dvl-tum/intra_batch_connections.
IBAFormer: Intra-batch Attention Transformer for Domain Generalized Semantic Segmentation
East China University of Science and Technology（华东理工大学）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；United Imaging Intelligence（联影智能）；Linköping University（林雪平大学）
Segmentation of Intracranial Arterial Calcification with Deeply Supervised Residual Dropout Networks
Johns Hopkins University（约翰斯·霍普金斯大学）；Erasmus MC（伊拉斯姆斯大学医学中心）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；University of Copenhagen（哥本哈根大学）；Technische Universität München（慕尼黑工业大学）
Intrinsically Stable Spiking Neural Networks: Overcoming the Performance Barrier in the Absence of Batch Normalization
University of Electronic Science and Technology of China (UESTC)（电子科技大学）；Beijing Institute of Remote-Sensing Equipment（北京遥感设备研究所）；Shenzhen Institute for Advanced Study, UESTC（电子科技大学深圳高等研究院）
