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We present an approach to simultaneously perform semantic segmentation andprepositional phrase attachment resolution for captioned images. Someambiguities in language cannot be resolved without simultaneously reasoningabout an associated image. If we consider the sentence "I shot an elephant inmy pajamas", looking at language alone (and not using common sense), it isunclear if it is the person or the elephant wearing the pajamas or both. Ourapproach produces a diverse set of plausible hypotheses for both semanticsegmentation and prepositional phrase attachment resolution that are thenjointly reranked to select the most consistent pair. We show that our semanticsegmentation and prepositional phrase attachment resolution modules havecomplementary strengths, and that joint reasoning produces more accurateresults than any module operating in isolation. Multiple hypotheses are alsoshown to be crucial to improved multiple-module reasoning. Our vision andlanguage approach significantly outperforms the Stanford Parser (De Marneffe etal., 2006) by 17.91% (28.69% relative) and 12.83% (25.28% relative) in twodifferent experiments. We also make small improvements over DeepLab-CRF (Chenet al., 2015).

Resolving Language and Vision Ambiguities Together: Joint Segmentation & Prepositional Attachment Resolution in Captioned Scenes