MoAI: Mixture of All Intelligence for Large Language and Vision Models (AI summary)

Key Points

- The paper introduces a new Large Language and Vision Model (LLVM), Mixture of All Intelligence (MoAI), designed to improve real-world scene understanding in vision language tasks such as object recognition, detection, scene graph generation, and optical character recognition (OCR).

- MoAI operates through two newly introduced modules: MoAI-Compressor, which aligns and condenses the outputs of external computer vision (CV) models, and MoAI-Mixer, which blends visual, auxiliary, and language features utilizing the concept of Mixture of Experts to significantly outperform existing LLVMs in various real-world scene understanding tasks.

- The paper discusses the rise of instruction-tuned Large Language Models (LLMs) and LLVMs and the trend of using instruction tuning datasets and enlarging capacities to improve zero-shot performance across language and vision tasks.

- It highlights the limitations of existing LLVMs in leveraging detailed and comprehensive real-world scene understanding available from specialized computer vision (CV) models and presents MoAI as a solution to enhance visual perception capabilities without the need for additional dataset curation or model scaling.

- The paper explains the use of external CV models such as panoptic segmentation, open-world object detection, scene graph generation, and OCR to obtain auxiliary visual information for MoAI and presents the architecture of MoAI-Compressor and MoAI-Mixer that effectively utilize these inputs.

- It describes the training steps, implementation details, and evaluation of MoAI’s visual perception capability, demonstrating its exceptional performance in various zero-shot vision language benchmarks, surpassing both open-source and closed-source LLVMs.

- Ablation studies are conducted to validate the effectiveness of the external CV models and MoAI-Mixer and gating networks, highlighting the crucial role of each external CV model in real-world scene understanding based on perception scores.

- The study provides insights regarding the importance of prioritizing real-world scene understanding and the potential of MoAI to achieve improved visual perception capabilities without the need for large-scale model or dataset scaling.

- The paper outlines future directions for MoAI, including incorporating more external CV models, addressing common-sense knowledge and low-level vision tasks, and emphasizes the potential of MoAI to advance LLVM modeling by effectively leveraging diverse auxiliary visual information and integrating multiple forms of intelligence.

Summary

The paper introduces a new large language and vision model (LLVM) called Mixture of All Intelligence (MoAI) that addresses the limitations of existing LLVMs in capturing detailed real-world scene understanding. This is achieved by leveraging auxiliary visual information from external segmentation, detection, scene graph generation, and optical character recognition (OCR) models through newly introduced modules: MoAI-Compressor and MoAI-Mixer. MoAI significantly outperforms current LLVMs in zero-shot vision language tasks related to real-world scene understanding, such as object existence, positions, relations, and OCR, without the need to enlarge the model size or curate extra visual instruction tuning datasets. This is achieved through the integration of visual, auxiliary, and language features using the Mixture of Experts concept.

Trend of Instruction-Tuned LLVMs and Introduction of MoAI Architecture
The paper discusses the trend of instruction-tuned large language and vision models (LLVMs) and the limitations of existing LLVMs in capturing detailed real-world scene understanding. It presents MoAI as a solution that leverages auxiliary visual information from external CV models to significantly outperform other LLVMs in zero-shot vision language tasks related to real-world scene understanding. The paper also introduces the architectural details of MoAI, including the MoAI-Compressor and MoAI-Mixer modules, and the technical implementation details. Additionally, the paper presents the availability of the MoAI code on GitHub for further exploration and use by the research community.

Validation of MoAI's Effectiveness and Impact
Through detailed evaluations, the paper validates the effectiveness of MoAI in real-world scene understanding tasks, exceeding the performance of other state-of-the-art open-source and closed-source LLVMs. The paper also includes ablation studies to illustrate the importance of each component in MoAI, as well as the significance of leveraging external CV models. The paper concludes with a discussion of the impact of MoAI and its potential for advancing LLVM modeling by effectively leveraging diverse auxiliary visual information and integrating multiple forms of intelligence.

Overall, the paper highlights the importance of real-world scene understanding, the enhancements achieved by MoAI in zero-shot vision language tasks, the effectiveness of MoAI in utilizing external CV models, and its potential to advance LLVM modeling.

Reference: https://arxiv.org/abs/2403.07508