This article discusses the development of a new method for image classification and segmentation utilizing hyperbolic geometry. The authors assess the effectiveness of their approach, particularly in fine-tuning vs. full-training models. With experimental evidence, they show that fine-tuning offers a superior understanding of visual scene structures, which is critical for various computer vision tasks. Additional visualizations included in the study elucidate the hierarchical nature of feature representation in Convolutional Neural Networks (CNNs), enhancing comprehension of model behavior and performance.
The experimental results demonstrate that the fine-tuning scheme is more suitable than full-training for understanding the structural organization of visual scenes.
We will provide additional visualization results included in the main paper to examine visual hierarchy in CNNs.
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