Improving Visual Recognition with Hyperbolical Visual Hierarchy Mapping
Hyeongjun Kwon Jinhyun Jang Jin Kim Kwonyoung Kim Kwanghoon Sohn
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.