Few-shot class-incremental learning (FSCIL) is designed to incrementally recognize novel
classes with only few training samples after the (pre-) training on base classes with sufficient …

Contrastive learning models based on Siamese structure have demonstrated remarkable
performance in self-supervised learning. Such a success of contrastive learning relies on …

Comprehensive 3D scene understanding, both geometrically and semantically, is important
for real-world applications such as robot perception. Most of the existing work has focused …

Few-shot remote sensing scene classification tries to make a model quickly adapt to new
scenes with only a few samples that do not appear in the closed training set. Since limited …

Few-shot learning is an extremely challenging task in computer vision that has attracted
increased research attention in recent years. However, most recent methods do not fully use …

Few-shot learning, which aims to learn the concept of novel category from extremely limited
labeled samples, has received intense interests in remote sensing image scene …

Multi-task visual learning is a critical aspect of computer vision. Current research, however,
predominantly concentrates on the multi-task dense prediction setting, which overlooks the …

Background Natural language processing (NLP) has become an emerging technology in
health care that leverages a large amount of free-text data in electronic health records to …

Generative modeling has recently shown great promise in computer vision, but it has mostly
focused on synthesizing visually realistic images. In this paper, motivated by multi-task …

Abstract Representation learning models employing Siamese structures have consistently
demonstrated exceptional performance across various fields, including deep learning …