mlsanityData to 14 Oct 2026

MRD: Multi-resolution Retrieval-Detection Fusion for High-Resolution Image Understanding

arXiv 2512.02906 ↗First posted 2 Dec 2025cs.CV cs.AI cs.MMcited by 1

On the map: Multimodal LLMs / vision-language models → Document understanding and multimodal RAG

Abstract (arXiv)

Understanding high-resolution (HR) images remains a critical challenge for multimodal large language models (MLLMs). Recent approaches leverage vision-based retrieval-augmented generation (RAG) to retrieve query-relevant crops from HR images, improving understanding capacity of MLLMs. However, this paradigm often leads to object fragmentation, resulting in semantic bias and incomplete retrieval, while also introducing false positives from irrelevant background patches. To address these issues, we propose Multi-resolution Retrieval-Detection (MRD), a training-free framework that enhances HR image understanding from both local and global perspectives. Locally, MRD enforces cross-scale semantic consistency via multi-resolution semantic fusion to mitigate single-resolution bias and alleviate object fragmentation. Globally, it integrates open-vocabulary object detection (OVD) as localization priors within a unified framework. Extensive experiments across multiple MLLMs on HR image benchmarks demonstrate that MRD achieves state-of-the-art (SOTA) performance on both single-object and multi-object understanding tasks. Code will be available at: https://github.com/yf0412/MRD.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Document understanding and multimodal RAG, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 58 of its 61 references that are in the map. First posted on arXiv 2 Dec 2025.

Builds on

58 of 61 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Retrieval-Augmented Perception: High-Resolution Image Perception Meets Visual RAG3 Mar
  2. 2023V*: Guided Visual Search as a Core Mechanism in Multimodal LLMs21 Dec

Other references · 56

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026ActiveScope: Actively Seeking and Correcting Perception for MLLMscited by 1

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  8. 2026Look Where It Matters: High-Resolution Crops Retrieval for Efficient VLMs