Summary of Pics: Pipeline For Image Captioning and Search, by Grant Rosario et al.
PICS: Pipeline for Image Captioning and Search
by Grant Rosario, David Noever
First submitted to arxiv on: 1 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Information Retrieval (cs.IR); 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 A novel approach called PICS (Pipeline for Image Captioning and Search) is introduced to efficiently categorize and retrieve large-scale image repositories. Leveraging Large Language Models (LLMs), PICS automates image captioning, surpassing traditional manual annotation methods. By generating AI-powered captions and integrating sentiment analysis, PICS enhances metadata, enabling nuanced searches beyond basic descriptors. This methodology simplifies managing vast image collections while setting a new precedent for accuracy and efficiency in image retrieval. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PICS is a way to organize lots of images quickly and easily. It uses special computer models to write descriptions of each picture, which makes it easier to find the right one later. This helps keep track of huge collections of pictures and makes searching for specific ones faster and more accurate. |
Keywords
* Artificial intelligence * Image captioning
