Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

Object segmentation and object tracking are fundamental research areas in the computer
vision community. These two topics are difficult to handle some common challenges, such …

Video-language pre-training (VLP) has become increasingly important due to its ability to
generalize to various vision and language tasks. However, existing egocentric VLP …

Video summarization technologies aim to create a concise and complete synopsis by
selecting the most informative parts of the video content. Several approaches have been …

We address the problem of retrieving a specific moment from an untrimmed video by a query
sentence. This is a challenging problem because a target moment may take place in …

This paper addresses the problem of temporal sentence grounding (TSG), which aims to
identify the temporal boundary of a specific segment from an untrimmed video by a sentence …

Video summarization aims to facilitate large-scale video browsing by producing short,
concise summaries that are diverse and representative of original videos. In this paper, we …

Perceiving meaningful activities in a long video sequence is a challenging problem due to
ambiguous definition ofmeaningfulness' as well as clutters in the scene. We approach this …

In this paper, we propose a Detect-to-Summarize network (DSNet) framework for supervised
video summarization. Our DSNet contains anchor-based and anchor-free counterparts. The …

We propose a novel supervised learning technique for summarizing videos by automatically
selecting keyframes or key subshots. Casting the task as a structured prediction problem …