Object-Aware Token Merging Boosts Vision-Language Retrieval Efficiency

Jul 14, 2026

Researchers introduce SaMer, an object-aware token merging framework for multi-vector vision-language retrieval. SaMer compresses image-side tokens by over 93% while improving retrieval accuracy on benchmarks like Flickr30K and MSCOCO. The method preserves object-level evidence needed for effective retrieval, outperforming existing compression baselines and enhancing phrase-level grounding.

Why it matters: This work demonstrates that preserving object evidence, rather than merely reducing token count, is crucial for efficient and accurate multi-vector vision-language retrieval, enabling substantial storage and computation savings.

Full story at: arXiv Information Retrieval ↗