Deep learning-based video anomaly detection methods have drawn significant attention in
the past few years due to their superior performance. However, almost all the leading …

Multimodal sentiment analysis has a wide range of applications due to its information
complementarity in multimodal interactions. Previous works focus more on investigating …

We address the problem of extracting key steps from unlabeled procedural videos,
motivated by the potential of Augmented Reality (AR) headsets to revolutionize job training …

Multimedia summarization with multimodal output can play an essential role in real-world
applications, ie, automatically generating cover images and titles for news articles or …

This work introduces an unsupervised framework for video anomaly detection, leveraging a
hybrid deep learning model that combines a vision transformer (ViT) with a convolutional …

Video summarization is to extract effective information from videos to quickly obtain the most
informative summary. Most of the existing video summarization methods use recurrent …

In recent years, deep neural network architectures and learning algorithms have greatly
improved the performance of computer vision tasks. However, acquiring and annotating …

In this paper, we present a new process for creating video summaries in an unsupervised
manner. Our approach involves training a transformer encoder model to reconstruct missing …

Key frame extraction is essential for video retrieval because it reduces the quantity of data
needed to be processed. However, current video comparison methods classify videos by …

Multimedia summarization with multimodal output (MSMO) is a recently explored application
in language grounding. It plays an essential role in real-world applications, ie, automatically …