Concepts Complement Dense Semantics: Learning Compact Sparse Spaces for Text-Image Retrieval
Cross-modal retrieval has been advanced by vision-language pre-trained models that encode images and texts into a shared dense embedding space. While dense representations effectively capture overall semantic similarity, they often obscure fine-grained visual-textual information needed for precise cross-modal matching. Recent methods introduce a learned sparse branch to complement dense matching with lexical evidence, but they rely on a redundant language-model token space and lack explicit grounding for sparse dimensions. We propose GRASP, a compact and grounded sparse learning framework that mines visual-textual concepts from the corpus. A lightweight sparse head is trained to predict c...
| Authors | Yoonseo Kim, Jungwoo Choi, Cheonyoung Park et al. |
|---|---|
| Primary category | cs.IR |
| Published | 26 September 2026 |
| Last updated at source | 26 September 2026 |
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