Improving Visual Recognition with Hyperbolical Visual Hierarchy Mapping
<|reference_start|>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.<|reference_end|>
@article{kwon2024improving,
    title={Improving Visual Recognition with Hyperbolical Visual Hierarchy Mapping},
    author={Hyeongjun Kwon, Jinhyun Jang, Jin Kim, Kwonyoung Kim, Kwanghoon Sohn},
    journal={arXiv preprint arXiv:2404.00974},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00974},
    primaryClass={cs.CV}
}