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Natural disasters can have devastating consequences for communities, causing loss of life and significant economic damage. To mitigate these impacts, it is crucial to quickly and accurately identify situational awareness and actionable information useful for disaster relief and response organizations. In this paper, we study the use of advanced transformer and contrastive learning models for disaster image classification in a humanitarian context, with focus on state-of-the-art pre-trained vision transformers such as ViT, CSWin and a state-of-the-art pre-trained contrastive learning model, CLIP. We evaluate the performance of these models across various disaster scenarios, including in-domain and cross-domain settings, as well as few- shot learning and zero-shot learning settings. Our results show that the CLIP model outperforms the two transformer models (ViT and CSWin) and also ConvNeXts, a competitive CNN-based model resembling transformers, in all the settings. By improving the performance of disaster image classification, our work can contribute to the goal of reducing the number of deaths and economic losses caused by disasters, as well as helping to decrease the number of people affected by these events.
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Maven: a multimodal foundation model for supernova science
Abstract A common setting in astronomy is the availability of a small number of high-quality observations, and larger amounts of either lower-quality observations or synthetic data from simplified models. Time-domain astrophysics is a canonical example of this imbalance, with the number of supernovae observed photometrically outpacing the number observed spectroscopically by multiple orders of magnitude. At the same time, no data-driven models exist to understand these photometric and spectroscopic observables in a common context. Contrastive learning objectives, which have grown in popularity for aligning distinct data modalities in a shared embedding space, provide a potential solution to extract information from these modalities. We present Maven, the first foundation model for supernova science. To construct Maven, we first pre-train our model to align photometry and spectroscopy from 0.5 M synthetic supernovae using a contrastive objective. We then fine-tune the model on 4702 observed supernovae from the Zwicky transient facility. Maven reaches state-of-the-art performance on both classification and redshift estimation, despite the embeddings not being explicitly optimized for these tasks. Through ablation studies, we show that pre-training with synthetic data improves overall performance. In the upcoming era of the Vera C. Rubin observatory, Maven will serve as a valuable tool for leveraging large, unlabeled and multimodal time-domain datasets.
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- Award ID(s):
- 2019786
- PAR ID:
- 10560931
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Machine Learning: Science and Technology
- Volume:
- 5
- Issue:
- 4
- ISSN:
- 2632-2153
- Format(s):
- Medium: X Size: Article No. 045069
- Size(s):
- Article No. 045069
- Sponsoring Org:
- National Science Foundation
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