Summary of Mmfactory: a Universal Solution Search Engine For Vision-language Tasks, by Wan-cyuan Fan et al.
MMFactory: A Universal Solution Search Engine for Vision-Language Tasks
by Wan-Cyuan Fan, Tanzila Rahman, Leonid Sigal
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Recent advancements in vision-language models and fine-tuning techniques have led to the development of various general-purpose and special-purpose models for visual tasks. While these models offer flexibility and accessibility, they are not suitable for all tasks or applications. To address this limitation, researchers have introduced approaches like visual programming and multimodal large language models (LLMs) with integrated tools that enable program synthesis. However, these approaches overlook user constraints, produce test-time sample-specific solutions, and require low-level instructions. To overcome these limitations, the MMFactory framework is introduced, which includes model and metrics routing components acting as a solution search engine across various available models. MMFactory can suggest diverse pools of programmatic solutions by instantiating and combining visio-lingual tools from its model repository. Additionally, it proposes metrics and benchmarks performance/resource characteristics, allowing users to pick a solution that meets their unique design constraints. Experimental results demonstrate that MMFactory outperforms existing methods in delivering state-of-the-art solutions tailored to user problem specifications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a lot of different tools that can help you solve visual problems, but each tool is good at only one specific task. This makes it hard to find the right tool for the job. To fix this, researchers created MMFactory, a system that helps users pick the best tool for their problem by searching through all available models and suggesting the most suitable ones. The system also proposes ways to measure how well each solution works and how much resources it needs, so users can choose the one that fits their needs. This makes it easier for people to find the right solution for their visual problems. |
Keywords
» Artificial intelligence » Fine tuning
