CosmoCLIP: Generalizing Large Vision-Language Models for Astronomical Imaging

Raza Imam, Mohammed Talha Alam, Umaima Rahman, Mohsen Guizani, Fakhri Karray

CosmoCLIP: Generalizing Large Vision-Language Models for Astronomical Imaging: 5 upvotes on Hugging Face Daily Papers, #9 of 14 papers on 2024-07-11. Day-by-day upvote history.

Existing vision-text contrastive learning models enhance representation transferability and support zero-shot prediction by matching paired image and caption embeddings while pushing unrelated pairs apart. However, astronomical image-label datasets are significantly smaller compared to general image and label datasets available from the internet. We introduce CosmoCLIP, an astronomical image-text contrastive learning framework precisely fine-tuned on the pre-trained CLIP model using SpaceNet and BLIP-based captions. SpaceNet, attained via FLARE, constitutes ~13k optimally distributed images, while BLIP acts as a rich knowledge extractor. The rich semantics derived from this SpaceNet and BLIP descriptions, when learned contrastively, enable CosmoCLIP to achieve superior generalization across various in-domain and out-of-domain tasks. Our results demonstrate that CosmoCLIP is a straightforward yet powerful framework, significantly outperforming CLIP in zero-shot classification and image-text retrieval tasks.

Paper page on Hugging Face · arXiv

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