Hyperbolic Image Embeddings
This addresses a fundamental problem in computer vision for researchers and practitioners, but it appears incremental as it builds on existing embedding paradigms.
The paper tackles the problem of image classification, retrieval, and few-shot learning by proposing hyperbolic embeddings as a better alternative to Euclidean and spherical embeddings, though no concrete results or numbers are provided.
Computer vision tasks such as image classification, image retrieval and few-shot learning are currently dominated by Euclidean and spherical embeddings, so that the final decisions about class belongings or the degree of similarity are made using linear hyperplanes, Euclidean distances, or spherical geodesic distances (cosine similarity). In this work, we demonstrate that in many practical scenarios hyperbolic embeddings provide a better alternative.