Overview of the 2017 RedICA text-image matching (RICATIM) challenge

Luis Pellegrin, H. Escalante, Alicia Morales, E. Morales, C. Reyes-García
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引用次数: 1

Abstract

This paper describes the design and analysis of results of the 2017 RedICA: Text-Image Matching (RICATIM) challenge. This academic competition faces the image labeling problem (assigning words to images) as one binary classification. Motivated by recent success of representation learning, we built a data set for binary classification in which each instance is the learned representation of a pair of an image and a word. Instances are labeled as positive, if the word is relevant for describing the content of the image and negative otherwise. Thus, participants of the challenge had to develop binary classification methods to distinguish between relevant and irrelevant text-image matchings. The challenge attracted 43 participants, that provided quite original and competitive solutions. The performance obtained by the top ranked participants was impressive, improving the performance of the baseline considerably. In this paper we describe the approached problem, the challenge design (including data and evaluation protocol), and provide an overview of the results achieved by participants.
2017年RedICA文本图像匹配(RICATIM)挑战赛概述
本文描述了2017年RedICA:文本图像匹配(RICATIM)挑战赛的设计和结果分析。这个学术竞赛将图像标注问题(将单词分配给图像)作为一个二值分类。受最近表示学习成功的启发,我们建立了一个用于二值分类的数据集,其中每个实例都是图像和单词对的学习表示。如果单词与描述图像内容相关,则将实例标记为积极的,否则标记为消极的。因此,挑战的参与者必须开发二进制分类方法来区分相关和不相关的文本图像匹配。这项挑战吸引了43名参与者,他们提供了非常新颖和有竞争力的解决方案。排名靠前的参与者的表现令人印象深刻,大大提高了基线的表现。在本文中,我们描述了所处理的问题,挑战设计(包括数据和评估协议),并提供了参与者所取得的结果的概述。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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