Due to its automatic feature learning ability and high performance, deep learning has
gradually become the mainstream of artificial intelligence in recent years, playing a role in …

Conventional image classification methods usually require a large number of training
samples for the training model. However, in practical scenarios, the amount of available …

Unsupervised image-to-image translation methods learn to map images in a given class to
an analogous image in a different class, drawing on unstructured (non-registered) datasets …

In this article, we deal with the problem of object detection on remote sensing images.
Previous researchers have developed numerous deep convolutional neural network (CNN) …

Humans can quickly learn new visual concepts, perhaps because they can easily visualize
or imagine what novel objects look like from different views. Incorporating this ability to …

The increasing application of Artificial Intelligence (AI) in health and medicine has attracted
a great deal of research interest in recent decades. This study aims to provide a global and …

Despite the large volume of face recognition datasets, there is a significant portion of
subjects, of which the samples are insufficient and thus under-represented. Ignoring such …

Low-shot visual learning--the ability to recognize novel object categories from very few
examples--is a hallmark of human visual intelligence. Existing machine learning approaches …

The ability to quickly recognize and learn new visual concepts from limited samples enable
humans to quickly adapt to new tasks and environments. This ability is enabled by the …

It is a difficult task to classify images with multiple class labels using only a small number of
labeled examples, especially when the label (class) distribution is imbalanced. Emotion …