Summary of Exploring the Frontier Of Vision-language Models: a Survey Of Current Methodologies and Future Directions, by Akash Ghosh et al.
Exploring the Frontier of Vision-Language Models: A Survey of Current Methodologies and Future Directions
by Akash Ghosh, Arkadeep Acharya, Sriparna Saha, Vinija Jain, Aman Chadha
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 The advent of Large Language Models (LLMs) has revolutionized AI, but they’re limited by their focus on text processing. To bridge this gap, researchers have developed Vision-Language Models (VLMs), which excel in tasks like image captioning and visual question answering. Our comprehensive survey paper categorizes VLMs into three types: models for vision-language understanding, models generating textual outputs from multimodal inputs, and models accepting and producing multimodal inputs. We dissect each model, analyzing its architecture, training data, strengths, and limitations, providing readers with a deep understanding of its components. Additionally, we evaluate the performance of VLMs in various benchmark datasets, highlighting potential avenues for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) have changed the AI world, but they’re mostly good at processing text. To make them better, researchers combined LLMs with visual capabilities to create Vision-Language Models (VLMs). These advanced models are great at tasks like describing pictures and answering questions about what’s in a photo. Our paper looks at all these VLMs and sorts them into three groups. We’ll tell you about each model’s strengths and weaknesses, how it was trained, and what it can do. This will help you understand how VLMs work. |
Keywords
» Artificial intelligence » Image captioning » Language understanding » Question answering
