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
7. Conclusion
In this paper, we have presented a novel Visual Hierarchy Mapper (Hi-Mapper) that investigates the hierarchical organization of visual scenes. We have achieved the goal by newly defining tree-like structure with probability distribution and learning the hierarchical relations in hyperbolic space. We have incorporated the hierarchical interpretation into the contrastive loss and efficiently identified the visual hierarchy in a data-efficient manner. Through an effective hierarchy decomposition and encoding procedures, the identified hierarchy has been successfully deployed to the global visual representation, enhancing the structured understanding of an entire scene. Hi-Mapper has consistently improved the performance of the existing DNNs when integrated with them, and also has demonstrated the effectiveness on various dense predictions.
Acknowledgement. This research was supported by the Yonsei Signature Research Cluster Program of 2022 (2022- 22-0002).
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