{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/show-attend-and-tell-neural-image-caption","title":"Show, Attend and Tell: Neural Image Caption Generation with Visual Attention","arxiv_id":"1502.03044","date":"2015-02-10","proceeding":null,"authors":["Kelvin Xu","Jimmy Ba","Ryan Kiros","Kyunghyun Cho","Aaron Courville","Ruslan Salakhutdinov","Richard Zemel","Yoshua Bengio"],"abstract":"Inspired by recent work in machine translation and object detection, we\nintroduce an attention based model that automatically learns to describe the\ncontent of images. We describe how we can train this model in a deterministic\nmanner using standard backpropagation techniques and stochastically by\nmaximizing a variational lower bound. We also show through visualization how\nthe model is able to automatically learn to fix its gaze on salient objects\nwhile generating the corresponding words in the output sequence. We validate\nthe use of attention with state-of-the-art performance on three benchmark\ndatasets: Flickr8k, Flickr30k and MS COCO.","url_abs":"http://arxiv.org/abs/1502.03044v3","url_pdf":"http://arxiv.org/pdf/1502.03044v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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