BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Automatic evaluation of summaries using N-gram co-occurrence statistics
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
SimpleNLG: a realisation engine for practical applications
ENLG '09 Proceedings of the 12th European Workshop on Natural Language Generation
Baselines for Image Annotation
International Journal of Computer Vision
How many words is a picture worth? Automatic caption generation for news images
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Generating image descriptions using dependency relational patterns
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Collecting image annotations using Amazon's Mechanical Turk
CSLDAMT '10 Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk
Every picture tells a story: generating sentences from images
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Composing simple image descriptions using web-scale n-grams
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Corpus-guided sentence generation of natural images
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Baby talk: Understanding and generating simple image descriptions
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Midge: generating image descriptions from computer vision detections
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Collective generation of natural image descriptions
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
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In this paper, we address the problem of automatically generating a description of an image from its annotation. Previous approaches either use computer vision techniques to first determine the labels or exploit available descriptions of the training images to either transfer or compose a new description for the test image. However, none of them report results on the effect of incorrect label detection on the quality of the final descriptions generated. With this motivation, we present an approach to generate image descriptions from image annotation and show that with accurate object and attribute detection, human-like descriptions can be generated. Unlike any previous work, we perform an extensive task-based evaluation to analyze our results.