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 Abstract and 1 Introduction 2. Related Work 3. Hyperbolic Geometry 4. Method 4.1. Overview 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 5.3. Semantic segmentation 5.4. Visualization 6. Ablation studies and discussion 7. Conclusion and References A. Network Architecture B. Theoretical Baseline C. Additional Results D. Additional visualization 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. 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). Authors: 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 Abstract and 1 Introduction Abstract and 1 Introduction 2. Related Work 2. Related Work 3. Hyperbolic Geometry 3. Hyperbolic Geometry 4. Method 4.1. Overview 4.1. Overview 4.2. Probabilistic hierarchy tree 4.2. Probabilistic hierarchy tree 4.3. Visual hierarchy decomposition 4.3. Visual hierarchy decomposition 4.4. Learning hierarchy in hyperbolic space 4.4. Learning hierarchy in hyperbolic space 4.5. Visual hierarchy encoding 4.5. Visual hierarchy encoding 5. Experiments and 5.1. Image classification 5. Experiments and 5.1. Image classification 5.2. Object detection and Instance segmentation 5.2. Object detection and Instance segmentation 5.3. Semantic segmentation 5.3. Semantic segmentation 5.4. Visualization 5.4. Visualization 6. Ablation studies and discussion 6. Ablation studies and discussion 7. Conclusion and References 7. Conclusion and References A. Network Architecture A. Network Architecture B. Theoretical Baseline B. Theoretical Baseline C. Additional Results C. Additional Results D. Additional visualization D. Additional visualization 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. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

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Supplementary Materials for Study on Hyperbolic Visual Hierarchy Mapping

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