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 …

Machine learning has been highly successful in data-intensive applications but is often
hampered when the data set is small. Recently, Few-shot Learning (FSL) is proposed to …

Visual recognition in low-data regimes requires deep neural networks to learn generalized
representations from limited training samples. Recently, CLIP-based methods have shown …

Data augmentation has recently seen increased interest in NLP due to more work in low-
resource domains, new tasks, and the popularity of large-scale neural networks that require …

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 …

State-of-the-art semantic segmentation methods require sufficient labeled data to achieve
good results and hardly work on unseen classes without fine-tuning. Few-shot segmentation …

Deep learning-based models have been shown to outperform human beings in many
computer vision tasks with massive available labeled training data in learning. However …

Learning from a limited number of samples is challenging since the learned model can
easily become overfitted based on the biased distribution formed by only a few training …

Few-shot segmentation is challenging because objects within the support and query images
could significantly differ in appearance and pose. Using a single prototype acquired directly …

The long-tail distribution of the visual world poses great challenges for deep learning based
classification models on how to handle the class imbalance problem. Existing solutions …