Do All Visual Tokens Matter Equally? Object-Evidence Preserving Token Merging for Vision-Language Retrieval

Hugging Face Daily Papers Papers
vision-language-retrieval token-compression object-aware-merging late-interaction multi-vector-retrieval efficient-retrieval

Summary

Introduces SaMer, an object-aware token merging framework that compresses image-side tokens for vision-language retrieval while preserving object-level evidence, achieving significant storage reduction and improved retrieval performance.

Multi-vector vision-language retrieval preserves fine-grained visual evidence through maximum-similarity late interaction, but dense image-side tokens make storage and scoring expensive. Existing token compression methods reduce this cost, yet they can remove or collapse object- and region-level evidence that future query tokens may need to select. We propose SaMer, an object-aware token merging framework that compresses image-side post-projector tokens into K representative centroids while preserving the original late-interaction interface. SaMer uses object annotations only during training as a merge prior to discourage cross-instance mixing, requires no ground-truth bounding boxes or detectors at inference time, and adapts only the shared projection layer with frozen vision and language backbones. With K=64, SaMer removes more than 93% of image-side tokens and reduces ColPali storage by 16.09times, while improving R@1 on Flickr30K and MSCOCO. These gains arise because object-aware merging preserves query-selectable object evidence that pruning or feature-only pooling can remove or collapse. SaMer also outperforms compression baselines and shows stronger phrase-level grounding, suggesting that efficient multi-vector retrieval depends not only on reducing token count, but on preserving the evidence future query tokens need to select.
Original Article
View Cached Full Text

Cached at: 07/07/26, 06:42 AM

Paper page - Do All Visual Tokens Matter Equally? Object-Evidence Preserving Token Merging for Vision-Language Retrieval

Source: https://huggingface.co/papers/2607.04605

Abstract

Object-aware token merging framework SaMer compresses image-side tokens while preserving query-selectable visual evidence, achieving significant storage reduction and improved retrieval performance.

Multi-vectorvision-language retrievalpreserves fine-grained visual evidence through maximum-similaritylate interaction, but dense image-side tokens make storage and scoring expensive. Existingtoken compressionmethods reduce this cost, yet they can remove or collapse object- and region-level evidence that future query tokens may need to select. We propose SaMer, an object-aware token merging framework that compresses image-sidepost-projector tokensinto K representative centroids while preserving the original late-interaction interface. SaMer uses object annotations only during training as a merge prior to discourage cross-instance mixing, requires no ground-truth bounding boxes or detectors at inference time, and adapts only the shared projection layer with frozen vision and language backbones. With K=64, SaMer removes more than 93% of image-side tokens and reduces ColPali storage by 16.09times, while improving R@1 on Flickr30K and MSCOCO. These gains arise becauseobject-aware mergingpreserves query-selectable object evidence that pruning or feature-only pooling can remove or collapse. SaMer also outperforms compression baselines and shows strongerphrase-level grounding, suggesting that efficientmulti-vector retrievaldepends not only on reducing token count, but on preserving the evidence future query tokens need to select.

View arXiv pageView PDFProject pageGitHubAdd to collection

Get this paper in your agent:

hf papers read 2607\.04605

Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash

Models citing this paper0

No model linking this paper

Cite arxiv.org/abs/2607.04605 in a model README.md to link it from this page.

Datasets citing this paper0

No dataset linking this paper

Cite arxiv.org/abs/2607.04605 in a dataset README.md to link it from this page.

Spaces citing this paper0

No Space linking this paper

Cite arxiv.org/abs/2607.04605 in a Space README.md to link it from this page.

Collections including this paper0

No Collection including this paper

Add this paper to acollectionto link it from this page.

Similar Articles

From 2D Grids to 1D Tokens: Reforming Shared Representations for Multimodal Image Fusion

Hugging Face Daily Papers

This paper introduces a multimodal image fusion method that uses a 1D token interface from a pretrained image tokenizer to enhance global appearance coherence while preserving local details through selective token editing (STE). Experiments on four benchmarks show state-of-the-art performance in both global coherence and local fidelity.