Summary of Text-guided Alternative Image Clustering, by Andreas Stephan et al.
Text-Guided Alternative Image Clustering
by Andreas Stephan, Lukas Miklautz, Collin Leiber, Pedro Henrique Luz de Araujo, Dominik Répás, Claudia Plant, Benjamin Roth
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 Text-Guided Alternative Image Consensus Clustering (TGAICC) leverages user-specified interests via prompts to guide the discovery of diverse image clusterings. This method generates a clustering for each prompt, groups them using hierarchical clustering, and then aggregates them using consensus clustering. TGAICC outperforms image- and text-based baselines on four alternative image clustering benchmark datasets. Additionally, it provides text-based explanations of the alternative clusterings using count-based word statistics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re looking at a big pile of pictures, and you want to group similar ones together. But traditional methods can only do that one way. This paper shows how to use special computer models to find many different ways to group those pictures, based on what someone is interested in. It’s like asking Google to show you all the cat pictures, but instead it groups them into categories like “cats with hats” or “cats playing piano”. The new method is called Text-Guided Alternative Image Consensus Clustering (TGAICC), and it works really well. |
Keywords
» Artificial intelligence » Clustering » Hierarchical clustering » Prompt
