Author: Luis Luis Pellegrin
Luis Luis Pellegrin OCTAVIO LOYOLA GONZALEZ JOSE ORTIZ BEJAR MIGUEL ANGEL MEDINA PEREZ ANDRES EDUARDO GUTIERREZ RODRIGUEZ Eric Sadit Téllez Avila MARIO GRAFF GUERRERO SABINO MIRANDA JIMENEZ Daniela Moctezuma MAURICIO ALFONSO GARCIA LIMON ALICIA MORALES REYES CARLOS ALBERTO REYES GARCIA Eduardo Morales Manzanares Hugo Jair Escalante (2019)
This paper describes the design of the 2017 RedICA: Text-Image Matching (RICATIM) challenge, including the dataset generation, a complete analysis of results, and the descriptions of the top-ranked developed methods. The academic challenge explores the feasibility of a novel binary image classification scenario, where each instance corresponds to the concatenation of learned representations of an image and a word. Instances are labeled as positive if the word is relevant for describing the visual content of the image, and negative otherwise. This novel approach of the image classification problem poses an alternative scenario where any text-image pair can be represented in such space, so any word could be considered for describing an image. The proposed methods are diverse and competitive, showing considerable improvements over the proposed baselines.