The main goal of machine learning is the creation of self-learning algorithms in many areas
of human activity. It allows a replacement of a person with artificial intelligence in seeking to …

Despite the remarkable achievement of recent underwater image restoration techniques, the
lack of labeled data has become a major hurdle for further progress. In this work, we …

Abstract Vision Transformer (ViT) extends the application range of transformers from
language processing to computer vision tasks as being an alternative architecture against …

In deep learning, different kinds of deep networks typically need different optimizers, which
have to be chosen after multiple trials, making the training process inefficient. To relieve this …

Cross-modal retrieval methods build a common representation space for samples from
multiple modalities, typically from the vision and the language domains. For images and …

The recently proposed Sharpness-Aware Minimization (SAM) improves generalization by
minimizing a\textit {perturbed loss} defined as the maximum loss within a neighborhood in …

Convolutional neural network (CNN)-based methods are widely used in remote sensing
image scene classification and can obtain excellent performances. However, the stacked …

One of the most pressing challenges for the detection of face-manipulated videos is
generalising to forgery methods not seen during training while remaining effective under …

Aiming at the application of deep learning in fault diagnosis, mechanical rotating equipment
components are prone to failure under complex working environment, and the industrial big …

In this work, we study the large-scale pretraining of BERT-Large with differentially private
SGD (DP-SGD). We show that combined with a careful implementation, scaling up the batch …