With the rapid development of wearable cameras, it is now feasible to considerably increase
the collection of egocentric video for first-person visual perception. However, the …

Class-incremental learning is one of the most important settings for the study of Continual
Learning, as it closely resembles real-world application scenarios. With constrained memory …

The aim of continual learning is to learn new tasks continuously (ie, plasticity) without
forgetting previously learned knowledge from old tasks (ie, stability). In the scenario of online …

In this paper we focus on a challenging Online Task-Free Class Incremental Learning
(OTFCIL) problem. Different from the existing methods that continuously learn the feature …

Most existing 3D object classification and retrieval algorithms rely on one-off supervised
learning on closed 3D object sets and tend to provide rigid convolutional neural networks …

Continual learning is an emerging research branch of deep learning, which aims to learn a
model for a series of tasks continually without forgetting knowledge obtained from previous …

Data replay is a successful incremental learning technique for images. It prevents
catastrophic forgetting by keeping a reservoir of previous data original or synthesized to …

Class incremental learning (CIL) has been proposed to solve the problem of learning to
classify new classes while maintaining the performance on old classes. A typical strategy is …

In this paper, we investigate the problem of incremental learning for object counting, where a
method must learn to count a variety of object classes from a sequence of datasets. A naive …

Deep reinforcement learning methods exhibit impressive performance on a range of tasks
but still struggle on hard exploration tasks in large environments with sparse rewards. To …