CARES: Context-Aware Resolution Selector for VLMs
Moshe Kimhi International Business Machines, Nimrod Shabtay Tel Aviv University, Raja Giryes Apple and Tel Aviv University, Chaim Baskin Ben Gurion University of the Negev, Eli Schwartz International Business Machines
Large vision–language models (VLMs) commonly process images at native or high resolution to remain effective across tasks. This inflates visual tokens to 97-99% of total tokens, resulting in high compute and latency, even when low-resolution images would suffice. We introduce CARES—a Context-Aware Resolution Selector, a lightweight preprocessing module that, given an image–query pair, predicts the minimal sufficient input resolution. CARES uses a compact VLM (350M) to extract features and predict when a target pretrained VLM’s response converges to its peak ability to answer correctly. Though trained as a discrete classifier over a set of optional resolutions, CARES interpolates continuous resolutions at inference for fine-grained control. Across five multimodal benchmarks spanning documents and natural images, as well as diverse target VLMs, CARES preserves task performance while reducing compute by up to 80%.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表