Recent studies have drawn attention to the untapped potential of the" star
operation"(element-wise multiplication) in network design. While intuitive explanations …

Sign language recognition (SLR) is a weakly supervised task that annotates sign videos as
textual glosses. Recent studies show that insufficient training caused by the lack of large …

Spatio-temporal predictive learning is a learning paradigm that enables models to learn
spatial and temporal patterns by predicting future frames from given past frames in an …

Spatiotemporal predictive learning aims to generate future frames by learning from historical
frames. In this paper, we investigate existing methods and present a general framework of …

As the deep learning revolution marches on, self-supervised learning has garnered
increasing attention in recent years thanks to its remarkable representation learning ability …

Abstract Vision Transformers (ViT) have made many breakthroughs in computer vision tasks.
However, considerable redundancy arises in the spatial dimension of an input image …

Semi-supervised learning (SSL) has witnessed great progress with various improvements in
the self-training framework with pseudo labeling. The main challenge is how to distinguish …

Recently, Transformer-based models are taken much focus on solving the task of image
super-resolution (SR) due to their ability to achieve better performance. However, these …

Mixup data augmentation approaches have been applied for various tasks of deep learning
to improve the generalization ability of deep neural networks. Some existing approaches …

Spatiotemporal predictive learning is a learning paradigm that enables models to learn
spatial and temporal patterns by predicting future frames from given past frames in an …