Summary of Multi-level Cross-modal Alignment For Image Clustering, by Liping Qiu and Qin Zhang and Xiaojun Chen and Shaotian Cai
Multi-level Cross-modal Alignment for Image Clustering
by Liping Qiu, Qin Zhang, Xiaojun Chen, Shaotian Cai
First submitted to arxiv on: 22 Jan 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 The paper presents a novel approach to improve pseudo-labeling in cross-modal pre-training models for image clustering tasks. A Multi-level Cross-modal Alignment (MLCA) method is proposed, which builds a smaller but more semantic-aware space and aligns images and texts at instance, prototype, and semantic levels. Theoretical analysis shows that the MLCA method converges and reduces expected clustering risk. Experimental results on five benchmark datasets demonstrate its superiority over existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem with cross-modal pre-training models that can produce poor-quality pseudo-labels and hurt image clustering performance. To fix this, researchers created a new way to align images and text called Multi-level Cross-modal Alignment (MLCA). This method makes a more helpful space for learning and lines up images and text in different ways. Tests on several big datasets show that MLCA works better than old methods. |
Keywords
* Artificial intelligence * Alignment * Clustering
