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Monkey: Image Resolution and Text Label Are Important Things for Large Multi-modal Models (2311.06607v4)

Published 11 Nov 2023 in cs.CV, cs.AI, and cs.CL

Abstract: Large Multimodal Models (LMMs) have shown promise in vision-language tasks but struggle with high-resolution input and detailed scene understanding. Addressing these challenges, we introduce Monkey to enhance LMM capabilities. Firstly, Monkey processes input images by dividing them into uniform patches, each matching the size (e.g., 448x448) used in the original training of the well-trained vision encoder. Equipped with individual adapter for each patch, Monkey can handle higher resolutions up to 1344x896 pixels, enabling the detailed capture of complex visual information. Secondly, it employs a multi-level description generation method, enriching the context for scene-object associations. This two-part strategy ensures more effective learning from generated data: the higher resolution allows for a more detailed capture of visuals, which in turn enhances the effectiveness of comprehensive descriptions. Extensive ablative results validate the effectiveness of our designs. Additionally, experiments on 18 datasets further demonstrate that Monkey surpasses existing LMMs in many tasks like Image Captioning and various Visual Question Answering formats. Specially, in qualitative tests focused on dense text question answering, Monkey has exhibited encouraging results compared with GPT4V. Code is available at https://github.com/Yuliang-Liu/Monkey.

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Authors (9)
  1. Zhang Li (26 papers)
  2. Biao Yang (48 papers)
  3. Qiang Liu (405 papers)
  4. Zhiyin Ma (5 papers)
  5. Shuo Zhang (256 papers)
  6. Jingxu Yang (1 paper)
  7. Yabo Sun (3 papers)
  8. Yuliang Liu (82 papers)
  9. Xiang Bai (222 papers)
Citations (167)
View on Semantic Scholar

Summary

Analysis of the "Monkey" Multimodal Model for Enhanced Image and Text Processing

The paper introduces "Monkey," a novel multimodal model designed to extend the capabilities of Large Multimodal Models (LMMs) by addressing the challenge of handling high-resolution input and detailed scene comprehension in vision-language tasks. This work hinges on the integration of enhanced image processing techniques and sophisticated description generation methods, which together optimize the learning outcomes across various vision-language applications such as image captioning and visual question answering (VQA).

Methodological Advances

Enhanced Input Resolution:

Monkey's approach to enhancing resolution involves partitioning input images into uniform patches and processing them using a sliding window method. Each patch, scaled to match the resolution of 448×448 used in the original training of vision encoders, is handled independently. This technique allows processing of images up to 1344×896 pixels, thus preserving more detailed visual information. The utilization of independent adapters for each patch, integrated with LoRA-enhanced adjustments, mitigates the need for extensive pretraining typical in models like Qwen-VL, while maintaining computational efficiency.

Multi-level Description Generation:

Addressing the inadequacies of existing datasets, Monkey incorporates a multi-level description generation framework that compiles enriched caption datasets using diverse advanced systems. Leveraging models like BLIP2, PPOCR, and ChatGPT, Monkey synthesizes comprehensive descriptions by merging the outputs from these varied sources. This approach not only creates more contextually abundant annotations but also aligns closely with the semantic richness required for nuanced image-text correlations.

Experimental Validation

The paper provides extensive empirical evidence supporting the efficacy of Monkey's dual-strategy approach. The model demonstrates superior performance in a broad spectrum of benchmarks, surpassing existing models like GPT4V in complex VQA tasks, particularly those requiring dense text understanding. The competitive results, especially in the domain of document-oriented VQA, underscore the strength of high-resolution input in sharpening textual perception.

Theoretical and Practical Implications

The introduction of Monkey reflects a significant stride in multimodal model innovation, providing a scalable solution to high-resolution image processing that enhances detailed visual and textual comprehension. The findings suggest potential advancements in various applications, particularly those involving document analysis, complex scene understanding, and real-world text-intensive tasks. Practically, this research opens pathways for efficient integration of pre-existing multimodal models without exorbitant computational demands, paving the way for wide scalability and practical deployment.

Future Directions in AI Development

Future research could explore further optimization of adapter and patch handling strategies to reduce computational load even further while maintaining resolution quality. Expanding the multi-level description generation framework to include a broader array of datasets and environmental contexts may enhance contextual understanding. Additionally, integrating this model with other emerging AI technologies could expand its utility across new domains.

In conclusion, "Monkey" presents a robust framework that effectively combines high-resolution image processing with enriched contextual understanding, setting a benchmark for future developments in multimodal AI systems. The methodologies outlined provide valuable insights into achieving resolution and descriptive adequacy in LMMs without the disproportionate increase in resource expenditure.

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