4 papers
LoCA: Spatially-Aware Low-Rank Convolutional Adaptation of Vision Foundation Models
Sojung An, Junha Lee, Sujeong You +2
Pre-trained Vision Foundation Models (VFMs) provide strong visual representations for diverse downstream tasks. The key challenge of VFM adaptation stems from the prohibitive costs…
Dataset Distillation for Super-Resolution without Class Labels and Pre-trained Models
Sunwoo Cho, Yejin Jung, Nam Ik Cho +1
Training deep neural networks has become increasingly demanding, requiring large datasets and significant computational resources, especially as model complexity advances. Data dis…
Efficient Attention-Sharing Information Distillation Transformer for Lightweight Single Image Super-Resolution
Karam Park, Jae Woong Soh, Nam Ik Cho
Transformer-based Super-Resolution (SR) methods have demonstrated superior performance compared to convolutional neural network (CNN)-based SR approaches due to their capability to…
Self-Supervised Learning with Probabilistic Density Labeling for Rainfall Probability Estimation
Junha Lee, Sojung An, Sujeong You +1
Numerical weather prediction (NWP) models are fundamental in meteorology for simulating and forecasting the behavior of various atmospheric variables. The accuracy of precipitation…