Summary of Survey Of Large Multimodal Model Datasets, Application Categories and Taxonomy, by Priyaranjan Pattnayak et al.
Survey of Large Multimodal Model Datasets, Application Categories and Taxonomy
by Priyaranjan Pattnayak, Hitesh Laxmichand Patel, Bhargava Kumar, Amit Agarwal, Ishan Banerjee, Srikant Panda, Tejaswini Kumar
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); 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 Multimodal learning, an emerging field in artificial intelligence, combines diverse data types (text, images, audio, video) to create more versatile and robust systems. Inspired by human senses, this approach enables applications like text-to-video conversion, visual question answering, and image captioning. Recent developments in multimodal language models (MLLMs) rely on large-scale datasets for thorough testing and training. The study highlights various datasets for training, domain-specific tasks, and real-world applications, emphasizing the importance of benchmark datasets for assessing model performance, scalability, and applicability. As multimodal learning evolves, overcoming challenges will propel AI research and applications forward. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a world where machines can learn from many different types of data, like pictures, sounds, and words. This is called multimodal learning. It’s like how we humans can learn by seeing, hearing, or reading things. Researchers are working on creating better systems that can do this. They’re making big collections of data to help these systems learn and get better. These datasets will be important for testing how well the systems work and making them useful in real-life situations. |
Keywords
» Artificial intelligence » Image captioning » Question answering
