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A Multitask Learning Encoder-N-Decod...

Nina, Oliver.

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A Multitask Learning Encoder-N-Decoder Framework for Movie and Video Descriptions.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A Multitask Learning Encoder-N-Decoder Framework for Movie and Video Descriptions./
Author:	Nina, Oliver.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	144 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-05, Section: B.
Contained By:	Dissertations Abstracts International80-05B.
Subject:	Computer Engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=11005532
ISBN:	9780438591813

A Multitask Learning Encoder-N-Decoder Framework for Movie and Video Descriptions.
Nina, Oliver.

A Multitask Learning Encoder-N-Decoder Framework for Movie and Video Descriptions. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 144 p.

Source: Dissertations Abstracts International, Volume: 80-05, Section: B.

Thesis (Ph.D.)--The Ohio State University, 2018.

This item must not be sold to any third party vendors.

Learning visual feature representations for video analysis is non-trivial and requires a large amount of training samples and a proper generalization framework. Many of the current state of the art methods for video captioning and movie description rely on simple encoding mechanisms through recurrent neural networks to encode temporal visual information extracted from video data. We introduce a novel multitask encoder-n-decoder framework for automatic semantic description and captioning of video sequences. In contrast to current approaches, at training time our method relies on multiple distinct decoders to train a visual encoder in a multitask fashion. Our method shows improved performance over current SotA methods in several metrics on both multi-caption and single-caption datasets. Our method is the first method to use a multi-task approach for encoding video features. Furthermore, based on human subject evaluations, our method was ranked as the most helpful algorithm for the visually impaired finishing first place at the Large Scale Movie Description Challenge (LSMDC) in the movie captioning task in conjunction with the International Conference in Computer Vision (ICCV) 2017. Our method won the competition task among other top participating research groups worldwide and is currently the state of the art on automatic commercial movie description.

ISBN: 9780438591813Subjects--Topical Terms:

1567821
Computer Engineering.

A Multitask Learning Encoder-N-Decoder Framework for Movie and Video Descriptions.
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520 $a Learning visual feature representations for video analysis is non-trivial and requires a large amount of training samples and a proper generalization framework. Many of the current state of the art methods for video captioning and movie description rely on simple encoding mechanisms through recurrent neural networks to encode temporal visual information extracted from video data. We introduce a novel multitask encoder-n-decoder framework for automatic semantic description and captioning of video sequences. In contrast to current approaches, at training time our method relies on multiple distinct decoders to train a visual encoder in a multitask fashion. Our method shows improved performance over current SotA methods in several metrics on both multi-caption and single-caption datasets. Our method is the first method to use a multi-task approach for encoding video features. Furthermore, based on human subject evaluations, our method was ranked as the most helpful algorithm for the visually impaired finishing first place at the Large Scale Movie Description Challenge (LSMDC) in the movie captioning task in conjunction with the International Conference in Computer Vision (ICCV) 2017. Our method won the competition task among other top participating research groups worldwide and is currently the state of the art on automatic commercial movie description.
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