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DualFocus: Integrating Macro and Micro Perspectives in Multi-modal Large Language Models

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arxiv 2402.14767 v1 pith:KPTCNBD2 submitted 2024-02-22 cs.CV

Yuhang Cao , Pan Zhang , Xiaoyi Dong , Dahua Lin , Jiaqi Wang This is my paper · ORCID
classification cs.CV
keywords dualfocusmacromicromllmsdetailedmodelperspectivesintegrating
verification ladder T0 review T1 audit T2 compute T3 formal
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We present DualFocus, a novel framework for integrating macro and micro perspectives within multi-modal large language models (MLLMs) to enhance vision-language task performance. Current MLLMs typically singularly focus on inputs at a predefined resolution, resulting in deficiencies in detailed questions involving local regions. We introduced a DualFocus mechanism where the model concentrates on the image from a macro perspective, responses to the question, and identifies suitable sub-regions to zoom in for subsequent micro perspective analysis. Via the integration of answers from both macro and micro perspectives, the model is adept at addressing tasks that encompass global, detailed, and combined considerations. To endows the DualFocus mechanism in MLLMs, we curated a tailored dataset derived from the Visual Genome (VG) and adapted it to align with the training regimen of DualFocus. Through comparative studies across different model sizes and benchmarks, we demonstrate DualFocus's superiority in balancing detailed examination with holistic insight, significantly reducing hallucination instances in MLLMs and improving their performance in various vision-language tasks.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MentalThink: Shaping Thoughts in Mental SVG World

    cs.AI 2026-07 conditional novelty 7.0 of 10

    MLLMs that generate and render SVG sketches as multi-turn intermediate reasoning steps reach 55.1% on VSIBench and 76.0% on MindCube, far above the Qwen2.5-VL-7B backbone.

  2. BVS: Bayesian Visual Search with Multimodal Large Language Model for Fine-grained Perception

    cs.CV 2026-07 conditional novelty 6.0 of 10

    BVS combines early-stop attention rollout priors with a scale-aware non-stationary kernel and GP-UCB to locate tiny objects in UHR images more accurately and with fewer MLLM queries than prior visual-search methods.

  3. Zoom-Refine: Boosting High-Resolution Multimodal Understanding via Localized Zoom and Self-Refinement

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A training-free two-step pipeline, localize a bounding box then refine the answer from a high-resolution crop, improves MLLM accuracy on high-resolution image benchmarks.

  4. VisReason: A Large-Scale Dataset for Visual Chain-of-Thought Reasoning

    cs.CV 2025-11 conditional novelty 5.0 of 10

    Fine-tuning Qwen2.5-VL on VisReason, a 489K-example multi-round visual chain-of-thought dataset (165K with pseudo-depth), modestly improves LLM-judged visual reasoning scores, with caveats about self-referential 3D ev...

  5. VisCodex: Unified Multimodal Code Generation via Merging Vision and Coding Models

    cs.CL 2025-08 unverdicted novelty 5.0 of 10

    Merging a coding LLM into a vision-language model via task vectors yields an open-source multimodal coder that reaches near-GPT-4o performance on the authors' new benchmark.

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