4 accepted papers
Data augmentation (DA) stands out as a powerful technique to enhance the generalization capabilities of deep neural networks across diverse tasks. However, in low-level vision tasks, DA remains rudimentary (i.e., vanilla DA), facing a critical bottleneck due to information loss. In this paper, we in…
Data Augmentation (DA) is a powerful technique for enhancing the generalization capabilities of deep neural networks. It has been successfully employed in super-resolution research, resulting in improved performance. However, current DA methods often struggle to adapt effectively to light field supe…
Meta-learning has achieved remarkable success as a powerful paradigm for transferring learned knowledge from previous tasks. However, the lack of a large number of diverse and qualified tasks is the bottleneck of current meta-learning, which can easily lead to overfitting and therefore seriously hur…
Domain generalization (DG) aims to train a model on multiple source domains which can be well generalized to the unseen target domains. Currently, most DG techniques in vision tasks mainly focus on one of the three research lines (i.e., data manipulation, representation learning, and learning strate…