AdaMerge: Tuning-Free Patch Compression for Multi-Vector Visual Document Retrieval
Multi-vector visual document retrieval (VDR) models such as ColPali and ColNomic achieve strong accuracy by representing each document with hundreds to thousands of patch-level embeddings, at substantial storage and latency cost. Existing compression methods either prune unimportant patches or merge similar ones into clusters; the recent state-of-the-art merging method Prune-then-Merge (PtM) consistently outperforms pruning-only baselines at high compression, but requires a per-dataset cluster budget m to be tuned by grid search. We observe that the merge-cosine sequence produced by hierarchical clustering exhibits a sharp cliff separating mergeable redundancy from salient signal, and tha...
| Authors | Jianxin You, Kun Ni |
|---|---|
| Primary category | cs.CV |
| Published | 18 September 2026 |
| Last updated at source | 18 September 2026 |
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