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Goal-Driven Sequential Data Abstraction

Song, Yi-Zhe (2019) Goal-Driven Sequential Data Abstraction In: International Conference on Computer Vision (ICCV 2019), 27 Oct - 02 Nov 2019, Seoul, South Korea.

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Abstract

Automatic data abstraction is an important capability for both benchmarking machine intelligence and supporting summarization applications. In the former one asks whether a machine can ‘understand’ enough about the meaning of input data to produce a meaningful but more compact abstraction. In the latter this capability is exploited for saving space or human time by summarizing the essence of input data. In this paper we study a general reinforcement learning based framework for learning to abstract sequential data in a goal-driven way. The ability to define different abstraction goals uniquely allows different aspects of the input data to be preserved according to the ultimate purpose of the abstraction. Our reinforcement learning objective does not require human-defined examples of ideal abstraction. Importantly our model processes the input sequence holistically without being constrained by the original input order. Our framework is also domain agnostic – we demonstrate applications to sketch, video and text data and achieve promising results in all domains.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Song, Yi-Zhey.song@surrey.ac.uk
Date : 2019
Copyright Disclaimer : © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Related URLs :
Depositing User : Clive Harris
Date Deposited : 20 Aug 2019 10:09
Last Modified : 27 Oct 2019 02:08
URI: http://epubs.surrey.ac.uk/id/eprint/852447

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