MEXA: Towards General Multimodal Reasoning with Dynamic Multi-Expert Aggregation
MEXA is a new system that helps computers understand and reason about different types of information, like text, images, and sounds, without needing extra training. It smartly chooses the best experts for each task to give better answers.
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- 1 MEXA can handle various types of data inputs effectively.
- 2 It selects the right expert models based on the task at hand.
- 3 The system improves performance on complex reasoning tasks.
Introduction
The introduction discusses the advancements in multimodal learning and the challenges faced in developing a unified framework for effective reasoning across diverse modalities and tasks.
MEXA: Dynamic Multi-Modal Expert Aggregation
MEXA’s framework is introduced, detailing the organization of expert modules and the strategy for selecting and aggregating expert outputs based on input queries and reasoning complexity.
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General-Purpose Multimodal Reasoning
This section critiques existing multimodal reasoning systems that rely on fixed architectures, emphasizing the need for a more flexible and scalable approach like MEXA.
Skill-Specialized Mixture of Expert Models
MEXA’s innovative approach to routing queries to specialized expert models based on modality and task complexity is outlined, showcasing its modular design and dynamic adaptation capabilities.
Figures Explained
The paper’s visual material highlights the workflow and the main system components.
- Figure 1 :: Figure 1: Overview of the MEXA Architecture. Given the input task context and question, MEXA first employs an MLLM router (Sec. 3.2.2) to select the appropriate experts based on input modality and required reasoning skills.The aggregator (Sec. 3.2.3) then reasons over the outputs from the selected experts to generate the final answer.
- Figure 2 :: Video Video Subtitle.
- Fig 3 and: Fig 3 and Fig 4 show two qualitative examples from Video-MMMU and SQA3D, respectively. In the Video-MMMU example, our framework effectively selects the most relevant experts, including the video expert and the medical image expert. The aggregator then filters and prioritizes key information extracted by the medical image expert, allow-.
- Figure 4: Video Expert: This educational video, presented by Dr. Manu Krishnan from ProAnatomy, introduces and explains Smith’s fracture, a specific type of wrist injury. The video begins with a friendly visual of a child holding an X-ray frame over their torso, introducing the topic in an engaging and accessible way. The video defines Smith’s fracture as a fracture of the distal end of the radius, accompanied by ventral or volar displacement of the broken fragment.Medical Image Expert: Image in 0:32 frame presents lateral X-ray views of the wrist comparing two types of distal radius fractures: Smith’s fracture and Colles’ fracture. In the left panel, the X-ray shows a fracture at the distal end of the radius, with the distal fragment displaced volarly (anteriorly). In the right panel, the Colles’ fracture also involves the distal radius, but with dorsal (posterior) displacement of the distal fragment.
- Figure 3 :: Figure 3: A qualitative example of Video-MMMU.
Frequently Asked Questions
MEXA is a new system that helps computers understand and reason about different types of information, like text, images, and sounds, without needing extra training. It smartly chooses the best experts for each task to give better answers.
The introduction discusses the advancements in multimodal learning and the challenges faced in developing a unified framework for effective reasoning across diverse modalities and tasks.
MEXA can handle various types of data inputs effectively. It selects the right expert models based on the task at hand. The system improves performance on complex reasoning tasks.
Yes. PDFDigest can turn this paper into a structured explanation, key takeaways, visual summaries, and a narrated video when available.