1,185 research outputs found
Movie Description
Audio Description (AD) provides linguistic descriptions of movies and allows
visually impaired people to follow a movie along with their peers. Such
descriptions are by design mainly visual and thus naturally form an interesting
data source for computer vision and computational linguistics. In this work we
propose a novel dataset which contains transcribed ADs, which are temporally
aligned to full length movies. In addition we also collected and aligned movie
scripts used in prior work and compare the two sources of descriptions. In
total the Large Scale Movie Description Challenge (LSMDC) contains a parallel
corpus of 118,114 sentences and video clips from 202 movies. First we
characterize the dataset by benchmarking different approaches for generating
video descriptions. Comparing ADs to scripts, we find that ADs are indeed more
visual and describe precisely what is shown rather than what should happen
according to the scripts created prior to movie production. Furthermore, we
present and compare the results of several teams who participated in a
challenge organized in the context of the workshop "Describing and
Understanding Video & The Large Scale Movie Description Challenge (LSMDC)", at
ICCV 2015
The Long-Short Story of Movie Description
Generating descriptions for videos has many applications including assisting
blind people and human-robot interaction. The recent advances in image
captioning as well as the release of large-scale movie description datasets
such as MPII Movie Description allow to study this task in more depth. Many of
the proposed methods for image captioning rely on pre-trained object classifier
CNNs and Long-Short Term Memory recurrent networks (LSTMs) for generating
descriptions. While image description focuses on objects, we argue that it is
important to distinguish verbs, objects, and places in the challenging setting
of movie description. In this work we show how to learn robust visual
classifiers from the weak annotations of the sentence descriptions. Based on
these visual classifiers we learn how to generate a description using an LSTM.
We explore different design choices to build and train the LSTM and achieve the
best performance to date on the challenging MPII-MD dataset. We compare and
analyze our approach and prior work along various dimensions to better
understand the key challenges of the movie description task
A Dataset for Movie Description
Descriptive video service (DVS) provides linguistic descriptions of movies
and allows visually impaired people to follow a movie along with their peers.
Such descriptions are by design mainly visual and thus naturally form an
interesting data source for computer vision and computational linguistics. In
this work we propose a novel dataset which contains transcribed DVS, which is
temporally aligned to full length HD movies. In addition we also collected the
aligned movie scripts which have been used in prior work and compare the two
different sources of descriptions. In total the Movie Description dataset
contains a parallel corpus of over 54,000 sentences and video snippets from 72
HD movies. We characterize the dataset by benchmarking different approaches for
generating video descriptions. Comparing DVS to scripts, we find that DVS is
far more visual and describes precisely what is shown rather than what should
happen according to the scripts created prior to movie production
Automatic semantic video annotation in wide domain videos based on similarity and commonsense knowledgebases
In this paper, we introduce a novel framework for automatic Semantic Video Annotation. As this framework detects possible events occurring in video clips, it forms the annotating base of video search engine. To achieve this purpose, the system has to able to operate on uncontrolled wide-domain videos. Thus, all layers have to be based on generic features.
This framework aims to bridge the "semantic gap", which is the difference between the low-level visual features and the human's perception, by finding videos with similar visual events, then analyzing their free text annotation to find a common area then to decide the best description for this new video using commonsense knowledgebases.
Experiments were performed on wide-domain video clips from the TRECVID 2005 BBC rush standard database. Results from these experiments show promising integrity between those two layers in order to find expressing annotations for the input video. These results were evaluated based on retrieval performance
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