Improving Visual Recognition with Hyperbolical Visual Hierarchy Mapping

• URL: http://arxiv.org/abs/2404.00974v1
• Date: Mon, 1 Apr 2024 07:45:42 GMT
• Title: Improving Visual Recognition with Hyperbolical Visual Hierarchy Mapping
• Authors: Hyeongjun Kwon, Jinhyun Jang, Jin Kim, Kwonyoung Kim, Kwanghoon Sohn,
• Abstract summary: We propose a Visual Hierarchy Mapper (Hi-Mapper) 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.
• Score: 33.405667735101595
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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