Improving Visual Recognition with Hyperbolical Visual Hierarchy Mapping

Visual scenes are naturally organized in a hierarchy, where a coarse semantic is recursively comprised of several fine details.Exploring such a visual hierarchy is crucial to recognize the complex relations of visual elements, leading to a comprehensive scene understanding.In this paper, we propose a Visual Hierarchy Mapper (Hi-Mapper), a novel approach for enhancing the structured understanding of the pre-trained Deep Neural Networks (DNNs).Hi-Mapper investigates the hierarchical organization of the visual scene by 1) pre-defining a hierarchy tree through the encapsulation of probability densities;and 2) learning the hierarchical relations in hyperbolic space with a novel hierarchical contrastive loss.The pre-defined hierarchy tree recursively interacts with the visual features of the pre-trained DNNs through hierarchy decomposition and encoding procedures, thereby effectively identifying the visual hierarchy and enhancing the recognition of an entire scene.Extensive experiments demonstrate that Hi-Mapper significantly enhances the representation capability of DNNs, leading to an improved performance on various tasks, including image classification and dense prediction tasks.The code is available at \url{https://github.com/kwonjunn01/Hi-Mapper}.