Geometric Analysis of Self-Supervised Vision Representations for Semantic Image Retrieval
Content-based image retrieval (CBIR) systems enable users to search images based on visual content instead of relying on metadata. The text domain has benefited from vector search of representations created with unsupervised methods such as BERT. However, modern self-supervised learning methods for vision are mostly not reported in CBIR-related literature, instead relying on supervised models or multi-modal methods that align text and vision. We evaluate how the representations learned by modern self-supervised learning methods for vision perform under typical retrieval stacks that leverage vector databases and nearest neighbor search. Our evaluation reveals that the latent space geometry...
| Authors | Esteban Rodríguez-Betancourt, Edgar Casasola-Murillo |
|---|---|
| Primary category | cs.IR |
| Published | 27 April 2026 |
| Last updated at source | 27 April 2026 |
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