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GalaxAlign: Mimicking Citizen Scientists' Multimodal Guidance for Galaxy Morphology Analysis

Created by
  • Haebom

Author

Ruoqi Wang, Haitao Wang, Qiong Luo

Outline

GalaxAlign is a novel multimodal approach for galaxy morphology analysis. To overcome the high cost or low accuracy of existing methods, it was inspired by the way citizen scientists identify galaxies using text descriptions and schematic symbols. GalaxAlign uses a trimodal alignment framework that aligns three types of data—schematic symbols, text labels, and galaxy images—during the fine-tuning process. This enables effective fine-tuning without expensive pretraining, and demonstrates performance improvements for galaxy classification and similarity retrieval tasks.

Takeaways, Limitations

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Takeaways:
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Effectively fine-tuning common pre-trained models to astronomical tasks in a cost-effective manner.
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Improve accuracy by leveraging multimodal information (schematic symbols, text, images).
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Presenting effective learning strategies by mimicking the approaches of citizen scientists.
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Shows improved performance in galaxy classification and similarity search tasks.
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Limitations:
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Lack of detailed analysis of how the proposed method compares to other multi-modal approaches.
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Lack of performance analysis for specific types of galaxies or specific image qualities.
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Further research is needed on the generalizability of the schematic symbols and text labels used.
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Generalization performance needs to be verified on datasets other than real astronomical datasets.
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