Semi-supervised video object segmentation is a task of segmenting the target object in a
video sequence given only a mask annotation in the first frame. The limited information …

Video object segmentation (VOS) is a highly challenging problem, since the target object is
only defined by a first-frame reference mask during inference. The problem of how to capture …

Few shot semantic segmentation has been proposed to enhance the generalization ability of
traditional models with limited data. Previous works mainly focus on the supervised tasks …

Continual learning in real-world scenarios is a major challenge. A general continual
learning model should have a constant memory size and no predefined task boundaries, as …

Generative modeling based approaches have led to significant advances in generalized
zero-shot learning over the past few-years. These approaches typically aim to learn a …

While classical time series forecasting considers individual time series in isolation, recent
advances based on deep learning showed that jointly learning from a large pool of related …

In classification settings, models trained on certain classes may apply to unseen ones,
indicating learning beyond the original task. This concept is frequently utilized in transfer …

Recent state-of-the-art semi-supervised Video Object Segmentation (VOS) methods have
shown significant improvements in target object segmentation accuracy when information …

In Human Robot Interaction (HRI) scenarios, robot systems would benefit from an
understanding of the user's state, actions and their effects on the environments to enable …

As one of the key methods of scene understanding, video object segmentation can be used
in applications such as video conferencing, video editing and autonomous driving. Recently …