  The exponential growth of astronomical datasets provides an unprecedented
opportunity for humans to gain insight into the Universe. However, effectively
analyzing this vast amount of data poses a significant challenge. Astronomers
are turning to deep learning techniques to address this, but the methods are
limited by their specific training sets, leading to considerable duplicate
workloads too. Hence, as an example to present how to overcome the issue, we
built a framework for general analysis of galaxy images, based on a large
vision model (LVM) plus downstream tasks (DST), including galaxy morphological
classification, image restoration, object detection, parameter extraction, and
more. Considering the low signal-to-noise ratio of galaxy images and the
imbalanced distribution of galaxy categories, we have incorporated a
Human-in-the-loop (HITL) module into our large vision model, which leverages
human knowledge to enhance the reliability and interpretability of processing
galaxy images interactively. The proposed framework exhibits notable few-shot
learning capabilities and versatile adaptability to all the abovementioned
tasks on galaxy images in the DESI legacy imaging surveys. Expressly, for
object detection, trained by 1000 data points, our DST upon the LVM achieves an
accuracy of 96.7%, while ResNet50 plus Mask R-CNN gives an accuracy of 93.1%;
for morphology classification, to obtain AUC ~0.9, LVM plus DST and HITL only
requests 1/50 training sets compared to ResNet18. Expectedly, multimodal data
can be integrated similarly, which opens up possibilities for conducting joint
analyses with datasets spanning diverse domains in the era of multi-message
astronomy.
