Few-shot meta-learning has been recently reviving with expectations to mimic humanity's
fast adaption to new concepts based on prior knowledge. In this short communication, we …

Deep neural networks can achieve great successes when presented with large data sets
and sufficient computational resources. However, their ability to learn new concepts quickly …

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 …

Disentangled Representation Learning (DRL) aims to learn a model capable of identifying
and disentangling the underlying factors hidden in the observable data in representation …

Given new tasks with very little data---such as new classes in a classification problem or a
domain shift in the input---performance of modern vision systems degrades remarkably …

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 …

Few-shot learning, ie, learning novel concepts from few examples, is fundamental to
practical visual recognition systems. While most of existing work has focused on few-shot …

Few-shot object detection has made encouraging progress by reconstructing novel class
objects using the feature representation learned upon a set of base classes. However, an …

Existing object detection methods typically rely on the availability of abundant labelled
training samples per class and offline model training in a batch mode. These requirements …

Despite the success of deep neural networks, there are still many challenges in deep
representation learning due to the data scarcity issues such as data imbalance, unseen …