Papers › Neural Baby Talk

Neural Baby Talk

27 Mar 2018CVPR 2018 6arXiv:1803.09845archive 2025-07-28

Jiasen Lu, Jianwei Yang, Dhruv Batra, Devi Parikh

We introduce a novel framework for image captioning that can produce natural language explicitly grounded in entities that object detectors find in the image. Our approach reconciles classical slot filling approaches (that are generally better grounded in images) with modern neural captioning approaches (that are generally more natural sounding and accurate). Our approach first generates a sentence `template' with slot locations explicitly tied to specific image regions. These slots are then filled in by visual concepts identified in the regions by object detectors. The entire architecture (sentence template generation and slot filling with object detectors) is end-to-end differentiable. We verify the effectiveness of our proposed model on different image captioning tasks. On standard image captioning and novel object captioning, our model reaches state-of-the-art on both COCO and Flickr30k datasets. We also demonstrate that our model has unique advantages when the train and test distributions of scene compositions -- and hence language priors of associated captions -- are different. Code has been made available at: https://github.com/jiasenlu/NeuralBabyTalk

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conv3x3 jiasenlu/NeuralBabyTalk/misc/resnet.py official repository ran · our draft was wrong MIT (permissive) · fac5364e2f53c6db · report
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Image CaptioningObjectSentenceSlot Fillingslot-filling

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