6 papers
Estimating Uncertainty in Galaxy Morphology Classification
Kai Cheng, Ruoqi Wang, Qiong Luo
Astronomers classify galaxy morphology to investigate cosmic evolution. While deep foundation models are increasingly utilized in Galaxy Morphology Classification (GMC), little wor…
Improving Radio Interferometry Imaging by Explicitly Modeling Cross-Domain Consistency in Reconstruction
Kai Cheng, Ruoqi Wang, Qiong Luo
Radio astronomy plays a crucial role in understanding the universe, particularly within the realm of non-thermal astrophysics. Images of celestial objects are derived from the sign…
D-GAP: Improving Out-of-Domain Robustness via Dataset-Agnostic and Gradient-Guided Augmentation in Frequency and Pixel Spaces
Ruoqi Wang, Haitao Wang, Shaojie Guo +1
Out-of-domain (OOD) robustness is challenging to achieve in real-world computer vision applications, where shifts in image background, style, and acquisition instruments always deg…
VVTRec: Radio Interferometric Reconstruction through Visual and Textual Modality Enrichment
Kai Cheng, Ruoqi Wang, Qiong Luo
Radio astronomy is an indispensable discipline for observing distant celestial objects. Measurements of wave signals from radio telescopes, called visibility, need to be transforme…
GalaxAlign: Mimicking Citizen Scientists' Multimodal Guidance for Galaxy Morphology Analysis
Ruoqi Wang, Haitao Wang, Qiong Luo
Galaxy morphology analysis involves studying galaxies based on their shapes and structures. For such studies, fundamental tasks include identifying and classifying galaxies in astr…
Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces
Ruoqi Wang, Haitao Wang, Shaojie Guo +1
Out-of-domain (OOD) robustness under domain adaptation settings, where labeled source data and unlabeled target data come from different distributions, is a key challenge in real-w…