Video captioning (VC) is a fast-moving, cross-disciplinary area of research that comprises
contributions from domains such as computer vision, natural language processing …

In this work, we introduce Vid2Seq, a multi-modal single-stage dense event captioning
model pretrained on narrated videos which are readily-available at scale. The Vid2Seq …

An ideal model for dense video captioning--predicting captions localized temporally in a
video--should be able to handle long input videos predict rich detailed textual descriptions …

Dense video captioning aims to generate multiple associated captions with their temporal
locations from the video. Previous methods follow a sophisticated" localize-then-describe" …

Generating an image/video caption has always been a fundamental problem of Artificial
Intelligence, which is usually performed using the potential of Deep Learning Methods …

Video captioning aims to describe the content of videos using natural language. Although
significant progress has been made, there is still much room to improve the performance for …

Contrastive learning allows us to flexibly define powerful losses by contrasting positive pairs
from sets of negative samples. Recently, the principle has also been used to learn cross …

Generating multi-sentence descriptions for video is considered to be the most complex task
in computer vision and natural language understanding due to the intricate nature of video …

Many real-world video-text tasks involve different levels of granularity, such as frames and
words, clip and sentences or videos and paragraphs, each with distinct semantics. In this …

Dense video captioning is a task of localizing interesting events from an untrimmed video
and producing textual description (captions) for each localized event. Most of the previous …