Summary of Model-based Cleaning Of the Quilt-1m Pathology Dataset For Text-conditional Image Synthesis, by Marc Aubreville et al.
Model-based Cleaning of the QUILT-1M Pathology Dataset for Text-Conditional Image Synthesis
by Marc Aubreville, Jonathan Ganz, Jonas Ammeling, Christopher C. Kaltenecker, Christof A. Bertram
First submitted to arxiv on: 11 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed pipeline for QUILT-1M dataset aims to predict common impurities in images and improve text-conditional image synthesis by removing noisy data. The authors suggest semantic alignment filtering to enhance image fidelity in text-to-image tasks, leading to a significant improvement in results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces the QUILT-1M dataset, which offers a wide range of online-sourced images with varying quality and composition. To make the most of this dataset, the researchers develop an automatic pipeline that identifies common impurities like narrators, desktop environments, or text within the images. By filtering out noisy data using semantic alignment, they demonstrate a substantial enhancement in image fidelity for text-to-image tasks. |
Keywords
» Artificial intelligence » Alignment » Image synthesis
