Recently, a number of competitive methods have tackled unsupervised representation learning by maximising the mutual information between the representations produced from augmentations. The resulting representations are then invariant to stochastic augmentation strategies, and can be used for downstream tasks such as clustering or classification. Yet data augmentations preserve many properties of an image and so there is potential for a suboptimal choice of representation that relies on matching easy-to-find features in the data. We demonstrate that greedy or local methods of maximising mutual information (such as stochastic gradient optimisation) discover local optima of the mutual information criterion; the resulting representations are also less-ideally suited to complex downstream tasks. Earlier work has not specifically identified or addressed this issue. We introduce deep hierarchical object grouping (DHOG) that computes a number of distinct discrete representations of images in a hierarchical order, eventually generating representations that better optimise the mutual information objective. We also find that these representations align better with the downstream task of grouping into underlying object classes. We tested DHOG on unsupervised clustering, which is a natural downstream test as the target representation is a discrete labelling of the data. We achieved new state-of-the-art results on the three main benchmarks without any prefiltering or Sobel-edge detection that proved necessary for many previous methods to work. We obtain accuracy improvements of: 4.3% on CIFAR-10, 1.5% on CIFAR-100-20, and 7.2% on SVHN.
H2G: Hierarchy-Aware Hyperbolic Grouping for 3D Scenes
Department of Computer Science and Engineering, Korea University（韩国大学计算机科学与工程系）；Intelligence and Interaction Research Center, Korea Institute of Science and Technology（韩国科学技术院智能与交互研究中心）
Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation
Universitat Politècnica de Catalunya, BarcelonaTech (UPC)（加泰罗尼亚理工大学（巴塞罗那理工））；Universidad de los Andes（洛斯安第斯大学）；University of California at Berkeley（加州大学伯克利分校）
PSNet: Fast Data Structuring for Hierarchical Deep Learning on Point Cloud
North University of China（中北大学）；Shanxi Information Industry Technology Research Institute Co., Ltd.（山西信息产业技术研究院有限公司）；University of Warwick（华威大学）；Shanxi University of Finance and Economics（山西财经大学）
MCbiF: Measuring Topological Autocorrelation in Multiscale Clusterings via 2-Parameter Persistent Homology
Department of Mathematics, Imperial College London, UK（英国伦敦帝国理工学院数学系）；Department of Mathematical Modeling and Machine Learning, University of Zurich, Switzerland（瑞士苏黎世大学数学建模与机器学习系）
Bodhi VLM: Privacy-Alignment Modeling for Hierarchical Visual Representations in Vision Backbones and VLM Encoders via Bottom-Up and Top-Down Feature Search
Auckland University of Technology（奥克兰技术大学）；Resideo Technologies, Inc.（Resideo技术公司）；Guilin University of Electronic Technology（桂林电子科技大学）；Universidad de Chile（智利大学）
