Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …

Multimodal classification research has been gaining popularity with new datasets in
domains such as satellite imagery, biometrics, and medicine. Prior research has shown the …

The ability to quickly learn a new task with minimal instruction-known as few-shot learning-is
a central aspect of intelligent agents. Classical few-shot benchmarks make use of few-shot …

Image and text matching plays a crucial role in bridging the cross-modal gap between vision
and language, and has achieved great progress due to the deep learning. However, the …

Recently, few-shot action recognition receives increasing attention and achieves remarkable
progress. However, previous methods mainly rely on limited unimodal data (eg, RGB …

Meta-learning has gained wide popularity as a training framework that is more data-efficient
than traditional machine learning methods. However, its generalization ability in complex …

Despite its astounding success in learning deeper multi-dimensional data, the performance
of deep learning declines on new unseen tasks mainly due to its focus on same-distribution …

Learning from large-scale contrastive language-image pre-training like CLIP has shown
remarkable success in a wide range of downstream tasks recently, but it is still under …

Deep learning is becoming increasingly relevant in drug discovery, from de novo design to
protein structure prediction and synthesis planning. However, it is often challenged by the …

Although significant progress has been made in few-shot learning, most of existing few-shot
image classification methods require supervised pre-training on a large amount of samples …