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• W2950401034 abstract "We introduce an inference technique to produce discriminative context-aware image captions (captions that describe differences between images or visual concepts) using only generic context-agnostic training data (captions that describe a concept or an image in isolation). For example, given images and captions of siamese cat and tiger cat, we generate language that describes the siamese cat in a way that distinguishes it from tiger cat. Our key novelty is that we show how to do joint inference over a language model that is context-agnostic and a listener which distinguishes closely-related concepts. We first apply our technique to a justification task, namely to describe why an image contains a particular fine-grained category as opposed to another closely-related category of the CUB-200-2011 dataset. We then study discriminative image captioning to generate language that uniquely refers to one of two semantically-similar images in the COCO dataset. Evaluations with discriminative ground truth for justification and human studies for discriminative image captioning reveal that our approach outperforms baseline generative and speaker-listener approaches for discrimination." @default.
• W2950401034 created "2019-06-27" @default.
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• W2950401034 date "2017-01-11" @default.
• W2950401034 modified "2023-10-16" @default.
• W2950401034 title "Context-aware Captions from Context-agnostic Supervision" @default.
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• W2950401034 doi "https://doi.org/10.48550/arxiv.1701.02870" @default.
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