Video summarization aims to generate a compact summary of the original video for efficient
video browsing. To provide video summaries which are consistent with the human …

Video summarization aims to generate a concise representation of a video capturing its
essential content and key moments while reducing its overall length. Although several …

In this paper, we propose a new task named incremental few-shot action recognition
(IFSAR), which aims to learn new action classes incrementally with limited samples. Existing …

Open set action recognition (OSAR) is a rising research domain that simultaneously
identifies all videos from known classes and rejects videos from unknown classes. Existing …

Video summarization is an essential problem in signal processing, which intends to produce
a concise summary of the original video. Existing video summarization approaches regard …

Video summarization remains a challenging task despite increasing research efforts.
Traditional methods focus solely on long-range temporal modeling of video frames …

Video summarization is an important topic studied by researchers. With the application of
deep learning, CNN and RNN have also been used to generate video summarization …

Adapting detection to different shot types is a significant challenge for video summarization
methods based on shot boundary detection. In our recent work, a new graph model was …

We propose the first Self-Attention Capsule Network that was designed to deal with unique
core challenges of medical imaging, specifically for tissue classification. These challenges …

In this thesis, we address the task of video summarization using unsupervised deep-learning
architectures. Video summarization aims to generate a short summary by selecting the most …