Video processing and analysis have become an urgent task, as a huge amount of videos
(eg, YouTube, Hulu) are uploaded online every day. The extraction of representative key …

The rapid advancement in text-to-video (T2V) generative models has enabled the synthesis
of high-fidelity video content guided by textual descriptions. Despite this significant progress …

Subspace clustering methods based on expressing each data point as a linear combination
of a few other data points (eg, sparse subspace clustering) have become a popular tool for …

Video contents are inherently heterogeneous. To exploit different feature modalities in a
diverse video collection for video summarization, we propose to formulate the task as a multi …

In this paper, we propose a new nonlinear dictionary learning (NDL) method and apply it to
image classification. While a variety of dictionary learning algorithms have been proposed in …

Video summarization (VS) is generally formulated as a subset selection problem where a set
of representative keyframes or key segments is selected from an entire video frame set …

Finding representative samples is important for predicting cancer risk. In particular, it is
crucial to identify each representative sample as responsible for the prediction performance …

Video Summarization (VS) has become one of the most effective solutions for quickly
understanding a large volume of video data. Dictionary selection with self representation …

Finding a small set of representatives from an unlabeled dataset is a core problem in a
broad range of applications such as dataset summarization and information extraction …

In this paper we tackle a problem of representative measuring points selection for
temperature field reconstruction. This problem is a version of the more general …