![Table 6. Performance comparisons between full-training and fine-tuning across various DNNs on the ImageNet-1K dataset [36].](https://hackernoon.imgix.net/images/fWZa4tUiBGemnqQfBGgCPf9594N2-80931zo.png?auto=format&fit=max&w=1200)
Feb 27, 2025
Supplementary Materials for Study on Hyperbolic Visual Hierarchy Mapping
by HyperboleMarch 1st, 2025
Too Long; Didn't Read
This document provides supplementary materials on Hyperbolic Visual Hierarchy Mapping, covering implementation details, theory, experiments, and visualizations.Company Mentioned
Authors:
(1) Hyeongjun Kwon, Yonsei University;
(2) Jinhyun Jang, Yonsei University;
(3) Jin Kim, Yonsei University;
(4) Kwonyoung Kim, Yonsei University;
(5) Kwanghoon Sohn, Yonsei University and Korea Institute of Science and Technology (KIST).
Table of Links
4. Method
4.2. Probabilistic hierarchy tree
4.3. Visual hierarchy decomposition
4.4. Learning hierarchy in hyperbolic space
4.5. Visual hierarchy encoding
5. Experiments and 5.1. Image classification
5.2. Object detection and Instance segmentation
6. Ablation studies and discussion
In this document, we include supplementary materials for “Improving Visual Recognition with Hyperbolical Visual Hierarchy Mapping”. We first provide more concrete implementation details (Sec. A), a theoretical baseline (Sec. B), and additional experimental results (Sec. C). Finally, we visualize more visual hierarchy trees from the selected images to provide solid evidence of the proposed method (Sec. D).
A. Network Architecture
A.1. Classification
A.2. Dense prediction.
B. Theoretical Baseline
B.1. Mixture of Gaussians
This paper is available on arxiv under CC BY 4.0 DEED license.
L O A D I N G
. . . comments & more!
. . . comments & more!
