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 …

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

The rapid development of deep learning provides great convenience for production and life.
However, the massive labels required for training models limits further development. Few …

In image classification, it is common practice to train deep networks to extract a single
feature vector per input image. Few-shot classification methods also mostly follow this trend …

In this paper, we introduce a challenging yet practical setting for person re-identification
(ReID) task, named lifelong person re-identification (LReID), which aims to continuously …

Few-shot classification is challenging because the data distribution of the training set can be
widely different to the test set as their classes are disjoint. This distribution shift often results …

While deep learning strategies achieve outstanding results in computer vision tasks, one
issue remains: The current strategies rely heavily on a huge amount of labeled data. In many …

Most few-shot learning techniques are pre-trained on a large, labeled" base dataset". In
problem domains where such large labeled datasets are not available for pre-training (eg, X …

While recent studies on semi-supervised learning have shown remarkable progress in
leveraging both labeled and unlabeled data, most of them presume a basic setting of the …

Semi-supervised few-shot learning is developed to train a classifier that can adapt to new
tasks with limited labeled data and a fixed quantity of unlabeled data. Most semi-supervised …