Visual Words Meet BM25: Sparse Auto-Encoder Visual Word Scoring for Image Retrieval
Dense image retrieval is accurate but offers limited interpretability and attribution, and it can be compute-intensive at scale. We present \textbf{BM25-V}, which applies Okapi BM25 scoring to sparse visual-word activations from a Sparse Auto-Encoder (SAE) on Vision Transformer patch features. Across a large gallery, visual-word document frequencies are highly imbalanced and follow a Zipfian-like distribution, making BM25's inverse document frequency (IDF) weighting well suited for suppressing ubiquitous, low-information words and emphasizing rare, discriminative ones. BM25-V retrieves high-recall candidates via sparse inverted-index operations and serves as an efficient first-stage retri...
| Authors | Donghoon Han, Eunhwan Park, Seunghyeon Seo |
|---|---|
| Primary category | cs.CV |
| Published | 6 March 2026 |
| Last updated at source | 6 March 2026 |
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