Summary of Text-guided Image Clustering, by Andreas Stephan et al.
Text-Guided Image Clustering
by Andreas Stephan, Lukas Miklautz, Kevin Sidak, Jan Philip Wahle, Bela Gipp, Claudia Plant, Benjamin Roth
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 to image clustering is proposed, which generates text descriptions of images using captioning and visual question-answering (VQA) models, and then clusters these text representations. The method introduces task- or domain knowledge for clustering by prompting VQA models, leading to improved performance on diverse datasets. Moreover, a counting-based cluster explainability method is proposed, which provides better descriptions of clusters than the cluster accuracy suggests. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Image clustering uses images to group similar pictures together. But what if we use words instead? Researchers are trying to do just that by generating text about images and then grouping those texts together. They’re using special models that can read and understand images, kind of like how you might describe a picture to a friend. The goal is to make image clustering better and more accurate. |
Keywords
* Artificial intelligence * Clustering * Prompting * Question answering
