6,410 research outputs found
Convolutional Hierarchical Attention Network for Query-Focused Video Summarization
Previous approaches for video summarization mainly concentrate on finding the
most diverse and representative visual contents as video summary without
considering the user's preference. This paper addresses the task of
query-focused video summarization, which takes user's query and a long video as
inputs and aims to generate a query-focused video summary. In this paper, we
consider the task as a problem of computing similarity between video shots and
query. To this end, we propose a method, named Convolutional Hierarchical
Attention Network (CHAN), which consists of two parts: feature encoding network
and query-relevance computing module. In the encoding network, we employ a
convolutional network with local self-attention mechanism and query-aware
global attention mechanism to learns visual information of each shot. The
encoded features will be sent to query-relevance computing module to generate
queryfocused video summary. Extensive experiments on the benchmark dataset
demonstrate the competitive performance and show the effectiveness of our
approach.Comment: Accepted by AAAI 2020 Conferenc
Collaborative Summarization of Topic-Related Videos
Large collections of videos are grouped into clusters by a topic keyword,
such as Eiffel Tower or Surfing, with many important visual concepts repeating
across them. Such a topically close set of videos have mutual influence on each
other, which could be used to summarize one of them by exploiting information
from others in the set. We build on this intuition to develop a novel approach
to extract a summary that simultaneously captures both important
particularities arising in the given video, as well as, generalities identified
from the set of videos. The topic-related videos provide visual context to
identify the important parts of the video being summarized. We achieve this by
developing a collaborative sparse optimization method which can be efficiently
solved by a half-quadratic minimization algorithm. Our work builds upon the
idea of collaborative techniques from information retrieval and natural
language processing, which typically use the attributes of other similar
objects to predict the attribute of a given object. Experiments on two
challenging and diverse datasets well demonstrate the efficacy of our approach
over state-of-the-art methods.Comment: CVPR 201
- …