BriefGPT.xyz
Nov, 2015
Sherlock: 图像中可扩展的事实学习
Sherlock: Modeling Structured Knowledge in Images
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Mohamed Elhoseiny, Scott Cohen, Walter Chang, Brian Price, Ahmed Elgammal
TL;DR
本研究提出了在图像中可扩展和统一地理解事实的方案,通过学习有结构的视觉事实,实现了一种均一且具有通用性的视觉理解,并在拓展了结构化事实的多个数据集以及超过202,000个事实和814,000个图像的大规模数据集中应用了所研究的方法。
Abstract
How to build a machine learning method that can continuously gain structured visual knowledge by learning
structured facts
? Our goal in this paper is to address this question by proposing a problem setting, where training data comes as
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