Large deep learning models are impressive, but they struggle when real-time data is not
available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for …

Forgetting refers to the loss or deterioration of previously acquired knowledge. While
existing surveys on forgetting have primarily focused on continual learning, forgetting is a …

Few-shot class incremental learning (FSCIL) portrays the problem of learning new concepts
gradually, where only a few examples per concept are available to the learner. Due to the …

New classes arise frequently in our ever-changing world, eg, emerging topics in social
media and new types of products in e-commerce. A model should recognize new classes …

In contrast to batch learning where all training data is available at once, continual learning
represents a family of methods that accumulate knowledge and learn continuously with data …

Few-shot image classification is a challenging problem that aims to achieve the human level
of recognition based only on a small number of training images. One main solution to few …

Few-shot class incremental learning--the problem of updating a trained classifier to
discriminate among an expanded set of classes with limited labeled data--is a key challenge …

The ability to incrementally learn new classes is vital to all real-world artificial intelligence
systems. A large portion of high-impact applications like social media, recommendation …

Incremental few-shot semantic segmentation (IFSS) targets at incrementally expanding
model's capacity to segment new class of images supervised by only a few samples …

Few-shot class-incremental learning (FSCIL) is challenged by catastrophically forgetting old
classes and over-fitting new classes. Revealed by our analyses, the problems are caused by …