AI看图时，不是所有像素都重要
现在的AI做图文检索，会把图片切成很多小方块（token），然后跟文字一一比对。但方块太多，存起来慢、算起来也慢。以前的方法直接砍掉一些方块，结果把关键物体也砍没了。这篇论文提出一个办法：训练时让AI知道哪些方块属于同一个物体（比如一只猫），然后把这些方块合并成一个代表，保留物体级别的证据。测试时不需要人工标框，AI自己就能认出物体。结果：只保留原来7%的方块，检索准确率反而更高，存储空间缩小16倍。它不是你明天就能用的工具，但说明了一个趋势：AI理解图片，不是像素越多越好，而是要知道哪些像素属于同一个东西。
📄 原文摘要(英文)
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.