567 research outputs found
Improving Sequential Determinantal Point Processes for Supervised Video Summarization
It is now much easier than ever before to produce videos. While the
ubiquitous video data is a great source for information discovery and
extraction, the computational challenges are unparalleled. Automatically
summarizing the videos has become a substantial need for browsing, searching,
and indexing visual content. This paper is in the vein of supervised video
summarization using sequential determinantal point process (SeqDPP), which
models diversity by a probabilistic distribution. We improve this model in two
folds. In terms of learning, we propose a large-margin algorithm to address the
exposure bias problem in SeqDPP. In terms of modeling, we design a new
probabilistic distribution such that, when it is integrated into SeqDPP, the
resulting model accepts user input about the expected length of the summary.
Moreover, we also significantly extend a popular video summarization dataset by
1) more egocentric videos, 2) dense user annotations, and 3) a refined
evaluation scheme. We conduct extensive experiments on this dataset (about 60
hours of videos in total) and compare our approach to several competitive
baselines
Text Summarization Techniques: A Brief Survey
In recent years, there has been a explosion in the amount of text data from a
variety of sources. This volume of text is an invaluable source of information
and knowledge which needs to be effectively summarized to be useful. In this
review, the main approaches to automatic text summarization are described. We
review the different processes for summarization and describe the effectiveness
and shortcomings of the different methods.Comment: Some of references format have update
Query-Focused Video Summarization: Dataset, Evaluation, and A Memory Network Based Approach
Recent years have witnessed a resurgence of interest in video summarization.
However, one of the main obstacles to the research on video summarization is
the user subjectivity - users have various preferences over the summaries. The
subjectiveness causes at least two problems. First, no single video summarizer
fits all users unless it interacts with and adapts to the individual users.
Second, it is very challenging to evaluate the performance of a video
summarizer.
To tackle the first problem, we explore the recently proposed query-focused
video summarization which introduces user preferences in the form of text
queries about the video into the summarization process. We propose a memory
network parameterized sequential determinantal point process in order to attend
the user query onto different video frames and shots. To address the second
challenge, we contend that a good evaluation metric for video summarization
should focus on the semantic information that humans can perceive rather than
the visual features or temporal overlaps. To this end, we collect dense
per-video-shot concept annotations, compile a new dataset, and suggest an
efficient evaluation method defined upon the concept annotations. We conduct
extensive experiments contrasting our video summarizer to existing ones and
present detailed analyses about the dataset and the new evaluation method
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