Zooming without Zooming: Region-to-Image Distillation for Fine-Grained Multimodal Perception
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.
698ea72fcace060ff123ae5d
Lai Wei
claimed_verified
2026-02-17T15:51:11.580Z
64a16b1aeacb4b50ba1c889d
https://cdn-avatars.huggingface.co/v1/production/uploads/64a16b1aeacb4b50ba1c889d/EhWoBGL6LlFGIVTUsp2F4.jpeg
Lai Wei
WaltonFuture
user
WaltonFuture
698ea72fcace060ff123ae5e
Liangbo He
698ea72fcace060ff123ae5f
Jun Lan
698ea72fcace060ff123ae60
Lingzhong Dong
698ea72fcace060ff123ae61
Yutong Cai
698ea72fcace060ff123ae62
Siyuan Li
698ea72fcace060ff123ae63
Huijia Zhu
698ea72fcace060ff123ae64
Weiqiang Wang
698ea72fcace060ff123ae65
Linghe Kong
698ea72fcace060ff123ae66
Yue Wang
698ea72fcace060ff123ae67
Zhuosheng Zhang
698ea72fcace060ff123ae68
Weiran Huang
Zooming without Zooming: Region-to-Image Distillation for Fine-Grained Multimodal Perception
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.
Region-to-Image Distillation enables fine-grained visual perception in MLLMs by training models to internally perform iterative zooming during inference, eliminating the need for repeated tool calls and visual re-encoding while maintaining high performance across multiple benchmarks.
Multimodal Large Language Models visual question answering fine-grained perception Thinking-with-Images region-to-image distillation micro-cropped regions teacher-student distillation ZoomBench visual reasoning GUI agents