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arXiv · 1711.06465

Grounding Visual Explanations (Extended Abstract)

Lisa Anne Hendricks·Ronghang Hu·Trevor Darrell·Zeynep Akata
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Abstract

Existing models which generate textual explanations enforce task relevance through a discriminative term loss function, but such mechanisms only weakly constrain mentioned object parts to actually be present in the image. In this paper, a new model is proposed for generating explanations by utilizing localized grounding of constituent phrases in generated explanations to ensure image relevance. Specifically, we introduce a phrase-critic model to refine (re-score/re-rank) generated candidate explanations and employ a relative-attribute inspired ranking loss using "flipped" phrases as negative examples for training. At test time, our phrase-critic model takes an image and a candidate explanation as input and outputs a score indicating how well the candidate explanation is grounded in the image.

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Lisa Anne Hendricks, Ronghang Hu, Trevor Darrell, Zeynep Akata. 2017-11-17. Grounding Visual Explanations (Extended Abstract). https://arxiv.org/abs/1711.06465

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