Zooming without Zooming: Region-to-Image Distillation for Fine-Grained Multimodal Perception

TL;DR

This paper introduces Region-to-Image Distillation, a training method that enables large multimodal models to perform fine-grained perception in a single inference pass, eliminating the need for iterative zooming during testing.

Contribution

It transforms zooming from an inference-time process into a training-time primitive, improving fine-grained perception without increased inference latency.

Findings

Achieves state-of-the-art results on fine-grained perception benchmarks.

Develops ZoomBench, a comprehensive VQA dataset for evaluation.

Demonstrates improved general multimodal cognition in experiments.

Abstract

Multimodal Large Language Models (MLLMs) excel at broad visual understanding but still struggle with fine-grained perception, where decisive evidence is small and easily overwhelmed by global context. Recent "Thinking-with-Images" methods alleviate this by iteratively zooming in and out regions of interest during inference, but incur high latency due to repeated tool calls and visual re-encoding. To address this, we propose Region-to-Image Distillation, which transforms zooming from an inference-time tool into a training-time primitive, thereby internalizing the benefits of agentic zooming into a single forward pass of an MLLM. In particular, we first zoom in to micro-cropped regions to let strong teacher models generate high-quality VQA data, and then distill this region-grounded supervision back to the full image. After training on such data, the smaller student model improves "single-glance" fine-grained perception without tool use. To rigorously evaluate this capability, we further present ZoomBench, a hybrid-annotated benchmark of 845 VQA data spanning six fine-grained perceptual dimensions, together with a dual-view protocol that quantifies the global--regional "zooming gap". Experiments show that our models achieve leading performance across multiple fine-grained perception benchmarks, and also improve general multimodal cognition on benchmarks such as visual reasoning and GUI agents. We further discuss when "Thinking-with-Images" is necessary versus when its gains can be distilled into a single forward pass. Our code is available at https://github.com/inclusionAI/Zooming-without-Zooming.