Summary of Effective Fine-tuning Of Vision-language Models For Accurate Galaxy Morphology Analysis, by Ruoqi Wang et al.
Effective Fine-Tuning of Vision-Language Models for Accurate Galaxy Morphology Analysis
by Ruoqi Wang, Haitao Wang, Qiong Luo
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Astrophysics of Galaxies (astro-ph.GA); Artificial Intelligence (cs.AI); 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 proposes GalaxAlign, a novel method to fine-tune pre-trained foundation models for galaxy morphology analysis. The approach combines the benefits of direct training on large datasets and fine-tuning on smaller sets of astronomical images. Specifically, GalaxAlign extends contrastive learning to align three types of data: schematic symbols representing galaxy shapes and structures, textual labels, and galaxy images. This method eliminates the need for expensive pre-training and enhances the effectiveness of fine-tuning. The authors demonstrate the efficacy of GalaxAlign through extensive experiments on galaxy classification and similarity search. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Galaxy morphology analysis is like trying to figure out what shape a galaxy is just by looking at pictures of it. Right now, scientists use two main methods: either they train special computers on lots of labeled galaxy pictures or they fine-tune pre-trained computer models on smaller sets of astronomical images. But these methods have limitations. To solve this problem, the authors propose a new way to fine-tune pre-trained computer models called GalaxAlign. This method uses three types of information: simple symbols that represent different galaxy shapes and structures, written descriptions of these symbols, and actual pictures of galaxies. By combining all this information, GalaxAlign can make more accurate predictions about galaxy shapes without needing a lot of training data. |
Keywords
» Artificial intelligence » Classification » Fine tuning
