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Feast Your Eyes: Mixture-of-Resolution Adaptation for Multimodal Large Language Models (2403.03003v1)

Published 5 Mar 2024 in cs.CV

Abstract: Despite remarkable progress, existing multimodal LLMs (MLLMs) are still inferior in granular visual recognition. Contrary to previous works, we study this problem from the perspective of image resolution, and reveal that a combination of low- and high-resolution visual features can effectively mitigate this shortcoming. Based on this observation, we propose a novel and efficient method for MLLMs, termed Mixture-of-Resolution Adaptation (MRA). In particular, MRA adopts two visual pathways for images with different resolutions, where high-resolution visual information is embedded into the low-resolution pathway via the novel mixture-of-resolution adapters (MR-Adapters). This design also greatly reduces the input sequence length of MLLMs. To validate MRA, we apply it to a recent MLLM called LLaVA, and term the new model LLaVA-HR. We conduct extensive experiments on 11 vision-language (VL) tasks, which show that LLaVA-HR outperforms existing MLLMs on 8 VL tasks, e.g., +9.4% on TextVQA. More importantly, both training and inference of LLaVA-HR remain efficient with MRA, e.g., 20 training hours and 3$\times$ inference speed than LLaVA-1.5. Source codes are released at: https://github.com/luogen1996/LLaVA-HR.

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Citations (34)
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Summary

  • The paper presents MRA, a dual-pathway design that integrates high- and low-resolution image features to enhance fine-grained visual recognition.
  • It employs MR-Adapters to efficiently fuse detailed semantic data with broader visual context, achieving a 9.4% TextVQA accuracy improvement and tripling training speed.
  • The approach broadens multimodal LLM applications in fields like autonomous driving and medical imaging by optimizing performance without sacrificing computational efficiency.

Mixture-of-Resolution Adaptation for Multimodal LLMs

The paper "Feast Your Eyes: Mixture-of-Resolution Adaptation for Multimodal LLMs" presents a novel technique to enhance the capabilities of multimodal LLMs (MLLMs) in fine-grained visual recognition tasks. This approach, named Mixture-of-Resolution Adaptation (MRA), tackles the challenges in visual content comprehension by leveraging both high- and low-resolution image features.

Core Contributions

At its core, MRA introduces a dual-pathway design for image encoding, which simultaneously processes high-resolution and low-resolution visual information. This parallel processing is augmented by the Mixture-of-Resolution Adapters (MR-Adapters), which effectively embed high-resolution data into the low-resolution pathway, leading to reduced input sequence lengths and enhanced performance in granular visual recognition tasks.

Methodological Insights

The dual visual pathways conceptually replicate the human vision system's global and local processing mechanisms. High-resolution pathways are dedicated to capturing detailed semantic information, whereas low-resolution pathways deal with broader visual contexts. This dual approach strategically aligns with prior research suggesting multifaceted processing strategies improve visual recognition outcomes (Merigan & Maunsell, 1993).

The MR-Adapters serve as a bridge between these two pathways, facilitating efficient information exchange and integration of fine-grained features into a cohesive visual representation.

Empirical Validation

The practical efficacy of MRA is demonstrated through its integration into an MLLM known as LLaVA, resulting in the enhanced model LLaVA-HR. Empirical results across 11 vision-language tasks showcase LLaVA-HR's superiority over existing models, with notable performance improvements in tasks such as TextVQA (+9.4% accuracy). Crucially, these enhancements do not come at the expense of computational efficiency. The findings report that LLaVA-HR achieves training and inference speeds three times faster than its non-adapted counterpart, LLaVA-1.5, underscoring the approach's cost-effectiveness.

Practical Implications

The introduction of MRA bears significant implications for the deployment of MLLMs in applications requiring high-resolution image comprehension, such as autonomous driving, medical imaging, and augmented reality. By maintaining computational efficiency while benefiting from high-resolution image data, MRA broadens the scope of MLLM utility in practical, resource-constrained environments.

Future Directions

Given the promising outcomes associated with MRA, future research may explore further optimization of resolution pathways or integration with more complex visual recognition models to address evolving, high-dimensional visual tasks. Moreover, expanding upon the dual-pathway framework to incorporate additional modalities could amplify the adaptability and robustness of multimodal models in diverse application scenarios.

This paper contributes a compelling advancement in the methodological arsenal for MLLMs, balancing the dual imperatives of resolution-intensive tasks’ performance and model efficiency. Its release of source codes facilitates reproducibility and paves the way for future exploration and innovation in the domain of vision-LLMing.

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  1. GitHub - luogen1996/LLaVA-HR: LLaVA-HR: High-Resolution Large Language-Vision Assistant (237 stars)
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