Summary of Evaluating Authenticity and Quality Of Image Captions Via Sentiment and Semantic Analyses, by Aleksei Krotov et al.
Evaluating authenticity and quality of image captions via sentiment and semantic analyses
by Aleksei Krotov, Alison Tebo, Dylan K. Picart, Aaron Dean Algave
First submitted to arxiv on: 14 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper investigates how deep learning models learn opinion (sentiment) from human-generated image captions in tasks like natural language processing and computer vision. The growth of deep learning relies heavily on large amounts of labelled data, but this raises concerns about the quality of the training data. Specifically, the study explores how the variety and diversity of provided captions impact a model’s learning process. The authors examine the influence of opinion learning on image-to-text or image-to-image pipelines, highlighting the importance of evaluating the quality of training data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning models can learn opinion (or sentiment) from human-generated image captions, which is important in tasks like natural language processing and computer vision. This paper looks at how this happens and why it matters. The authors want to know if having more or diverse captions helps a model learn better. They’re trying to figure out what makes a good training dataset. |
Keywords
» Artificial intelligence » Deep learning » Natural language processing
