Summary of Semantic-aware Representation Of Multi-modal Data For Data Ingress: a Literature Review, by Pierre Lamart et al.
Semantic-Aware Representation of Multi-Modal Data for Data Ingress: A Literature Review
by Pierre Lamart, Yinan Yu, Christian Berger
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper explores ways to efficiently manage large datasets in machine learning (ML) and generative AI, such as Large Language Models (LLMs). As datasets grow, so do the challenges of understanding their quality and diversity. The authors focus on semantic-aware techniques for extracting embeddings from mono-modal, multi-modal, and cross-modal data to improve information retrieval (IR) capabilities in growing data lakes. They investigate state-of-the-art methods and applications across three categories of data modalities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a world where computers can understand and process lots of different types of data, like words, pictures, sounds, and videos. Right now, it’s hard to make sense of all this data, so researchers are working on ways to efficiently manage it. One way is by using special techniques to extract important information from different types of data. This helps computers search for specific information more accurately. The study looks at how these techniques can be used in different situations and what benefits they bring. |
Keywords
» Artificial intelligence » Machine learning » Multi modal
