Compared to traditional machine learning, deep learning can learn deeper abstract data
representation and understand scattered data properties. It has gained considerable …

Change detection (CD) is an important Earth observation task that can monitor change
areas at two times from the view of space. However, fully supervised CD has a heavy …

The tasks of few-shot, one-shot, and zero-shot learning—or collectively “low-shot
learning”(LSL)—at first glance are quite similar to the long-standing task of class imbalanced …

Few-shot learning (FSL) algorithms are commonly trained through meta-learning (ML),
which exposes models to batches of tasks sampled from a meta-dataset to mimic tasks seen …

Human intelligence is characterized by our ability to absorb and apply knowledge from the
world around us, especially in rapidly acquiring new concepts from minimal examples …

Machine learning technology has a massive impact on society because it offers solutions to
solve many complicated problems like classification, clustering analysis, and predictions …

For real-world graph data, the node class distribution is inherently imbalanced and long-
tailed, which naturally leads to a few-shot learning scenario with limited nodes labeled for …

Imbalanced class distributions widely exist in real-world aerial images, which brings a
significant challenge to aerial scene classification due to the undesirable bias toward the …

Meta-learning is an effective way to deal with the few-shot problem that only very few
annotated data samples are available for training the model. In remote sensing scene …

While achieving remarkable success in remote sensing (RS) scene classification for the past
few years, convolutional neural network (CNN) based methods suffer from the demand for …