7,658 research outputs found
A Web Service for Video Summarization
This paper presents a Web service that supports the automatic generation of video summaries for user-submitted videos. The developed Web application decomposes the video into segments, evaluates the fitness of each segment to be included in the video summary and selects appropriate segments until a pre-defined time budget is filled. The integrated deep-learning-based video analysis and summarization technologies exhibit state-of-the-art performance and, by exploiting the processing capabilities of modern GPUs, offer faster than real-time processing. Configurations for generating video summaries that fulfill the specifications for posting on the most common video sharing platforms and social networks are available in the user interface of this application, enabling the one-click generation of distribution-channel-specific summaries
Co-Regularized Deep Representations for Video Summarization
Compact keyframe-based video summaries are a popular way of generating
viewership on video sharing platforms. Yet, creating relevant and compelling
summaries for arbitrarily long videos with a small number of keyframes is a
challenging task. We propose a comprehensive keyframe-based summarization
framework combining deep convolutional neural networks and restricted Boltzmann
machines. An original co-regularization scheme is used to discover meaningful
subject-scene associations. The resulting multimodal representations are then
used to select highly-relevant keyframes. A comprehensive user study is
conducted comparing our proposed method to a variety of schemes, including the
summarization currently in use by one of the most popular video sharing
websites. The results show that our method consistently outperforms the
baseline schemes for any given amount of keyframes both in terms of
attractiveness and informativeness. The lead is even more significant for
smaller summaries.Comment: Video summarization, deep convolutional neural networks,
co-regularized restricted Boltzmann machine
Visualizations for an Explainable Planning Agent
In this paper, we report on the visualization capabilities of an Explainable
AI Planning (XAIP) agent that can support human in the loop decision making.
Imposing transparency and explainability requirements on such agents is
especially important in order to establish trust and common ground with the
end-to-end automated planning system. Visualizing the agent's internal
decision-making processes is a crucial step towards achieving this. This may
include externalizing the "brain" of the agent -- starting from its sensory
inputs, to progressively higher order decisions made by it in order to drive
its planning components. We also show how the planner can bootstrap on the
latest techniques in explainable planning to cast plan visualization as a plan
explanation problem, and thus provide concise model-based visualization of its
plans. We demonstrate these functionalities in the context of the automated
planning components of a smart assistant in an instrumented meeting space.Comment: PREVIOUSLY Mr. Jones -- Towards a Proactive Smart Room Orchestrator
(appeared in AAAI 2017 Fall Symposium on Human-Agent Groups
A web assessment approach based on summarisation and visualisation
The number of Web sites has noticeably increased to roughly 224 million in last ten years. This means there is a rapid growth of information on the Internet. Although search engines can help users to filter their desired information, the searched result is normally presented in the form of a very long list, and users have to visit each Web page in order to determine the appropriateness of the result. This leads to a considerable amount of time has to be spent on finding the required information. To address this issue, this paper proposes a Web assessment approach in order to provide an overview of the information on a Website using an integration of existing summarisation and visualisation techniques, which are text summarisation, tag cloud, Document Type View, and interactive features. This approach is capable to reduce the time required to identify and search for information from the Web
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