Video summarization using deep neural networks: A survey
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 …
selecting the most informative parts of the video content. Several approaches have been …
Align and attend: Multimodal summarization with dual contrastive losses
The goal of multimodal summarization is to extract the most important information from
different modalities to form summaries. Unlike unimodal summarization, the multimodal …
different modalities to form summaries. Unlike unimodal summarization, the multimodal …
Clip-it! language-guided video summarization
M Narasimhan, A Rohrbach… - Advances in neural …, 2021 - proceedings.neurips.cc
A generic video summary is an abridged version of a video that conveys the whole story and
features the most important scenes. Yet the importance of scenes in a video is often …
features the most important scenes. Yet the importance of scenes in a video is often …
AC-SUM-GAN: Connecting actor-critic and generative adversarial networks for unsupervised video summarization
This paper presents a new method for unsupervised video summarization. The proposed
architecture embeds an Actor-Critic model into a Generative Adversarial Network and …
architecture embeds an Actor-Critic model into a Generative Adversarial Network and …
Generative Adversarial Networks (GANs) in Medical Imaging: Advancements, Applications and Challenges
AA Showrov, MT Aziz, HR Nabil, JR Jim… - IEEE …, 2024 - ieeexplore.ieee.org
Generative Adversarial Networks are a class of artificial intelligence algorithms that consist
of a generator and a discriminator trained simultaneously through adversarial training. GANs …
of a generator and a discriminator trained simultaneously through adversarial training. GANs …
Summarizing videos using concentrated attention and considering the uniqueness and diversity of the video frames
In this work, we describe a new method for unsupervised video summarization. To overcome
limitations of existing unsupervised video summarization approaches, that relate to the …
limitations of existing unsupervised video summarization approaches, that relate to the …
Progressive video summarization via multimodal self-supervised learning
Modern video summarization methods are based on deep neural networks that require a
large amount of annotated data for training. However, existing datasets for video …
large amount of annotated data for training. However, existing datasets for video …
Sumgraph: Video summarization via recursive graph modeling
The goal of video summarization is to select keyframes that are visually diverse and can
represent a whole story of an input video. State-of-the-art approaches for video …
represent a whole story of an input video. State-of-the-art approaches for video …
Multiple pairwise ranking networks for personalized video summarization
In this paper, we investigate video summarization in the supervised setting. Since video
summarization is subjective to the preference of the end-user, the design of a unique model …
summarization is subjective to the preference of the end-user, the design of a unique model …
Video summarization with a convolutional attentive adversarial network
With the explosive growth of video data, video summarization, which attempts to seek the
minimum subset of frames while still conveying the main story, has become one of the …
minimum subset of frames while still conveying the main story, has become one of the …