arXiv:2502.00241v2 Announce Type: replace-cross Abstract: Incorporating multiple modalities into large language models (LLMs) is a powerful way to enhance their understanding of non-textual data, enabling them to perform multimodal tasks. Vision language models (VLMs) form the fastest growing category of multimodal models because of their many practical use cases, including in healthcare, robotics, and accessibility. Unfortunately, even though different VLMs in the literature demonstrate impressive visual capabilities in different benchmarks, they are handcrafted by human experts; there is no
The rapid proliferation of multimodal models and their application-specific challenges necessitate automated solutions for optimal model selection, moving beyond expert-driven, manual approaches.
Automated model selection for Vision Language Models can significantly accelerate development, reduce costs, and democratize access to advanced AI capabilities across diverse industries.
The process of deploying and optimizing Vision Language Models will become more efficient and less reliant on specialized human expertise, leading to broader adoption and more sophisticated applications.
- · AI developers
- · Healthcare sector
- · Robotics industry
- · Accessibility technology providers
- · Manual model optimization consultants
- · Companies relying on outdated VLM deployment strategies
Faster deployment and iteration cycles for Vision Language Models across various applications.
Increased competition and innovation in application-specific VLM development due to lower barriers to entry.
The emergence of entirely new multimodal AI applications previously deemed too complex or costly to develop.
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