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

本论文提出了一种新颖的图像字幕生成模型，可在生成自然语言描述的同时，引入与图像实体检测相关的概念填充，通过生成带有显式图像区域链接的句子模板，并利用检测到的可视化概念填充这些区域，实现端到端的可微分框架，并在标准图像字幕生成和新物体字幕生成上达到了当前最先进水平。

神经婴儿语