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

Abstract Video Temporal Grounding (VTG), which aims to ground target clips from videos
(such as consecutive intervals or disjoint shots) according to custom language queries (eg …

This work proposes TimeChat a time-sensitive multimodal large language model specifically
designed for long video understanding. Our model incorporates two key architectural …

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 …

The exponential growth of technology has resulted in a profusion of advanced imaging
devices and eases internet accessibility, leading to an increase in the creation and use of …

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 …

Recently, video moment retrieval and highlight detection (MR/HD) are being spotlighted as
the demand for video understanding is drastically increased. The key objective of MR/HD is …

The goal of multimodal summarization is to extract the most important information from
different modalities to form summaries. Unlike unimodal summarization, the multimodal …

We consider retrieving a specific temporal segment, or moment, from a video given a natural
language text description. Methods designed to retrieve whole video clips with natural …