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Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

About

Inspired by recent work in machine translation and object detection, we introduce an attention based model that automatically learns to describe the content of images. We describe how we can train this model in a deterministic manner using standard backpropagation techniques and stochastically by maximizing a variational lower bound. We also show through visualization how the model is able to automatically learn to fix its gaze on salient objects while generating the corresponding words in the output sequence. We validate the use of attention with state-of-the-art performance on three benchmark datasets: Flickr8k, Flickr30k and MS COCO.

Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard Zemel, Yoshua Bengio• 2015

Related benchmarks

TaskDatasetResultRank
Radiology Report GenerationMIMIC-CXR (test)
BLEU-40.095
172
Image CaptioningMS-COCO (test)
CIDEr89
120
Image CaptioningFlickr30k (test)
CIDEr49.1
103
Medical Report GenerationMIMIC-CXR (test)
ROUGE-L0.31
62
Medical Report GenerationIU-Xray (test)
ROUGE-L0.307
56
Image CaptioningCOCO 2014 (test)
CIDEr0.878
44
Image CaptioningMS-COCO 2014 (test)
BLEU-425
43
Medical Report GenerationMIMIC-CXR
BLEU-40.088
43
Pathology report generationPathText BRCA (test)
BLEU-10.372
32
3D Dense CaptioningScanRefer (test)--
30
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