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In this paper, we address the task of natural language object retrieval, to localize a target object within a given image based on a natural language query of the object.
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Microsoft coco: Common objects in context
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R. Girshick · 2015
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A. Karpathy and L. Fei-Fei · 2015
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M. Ren, R. Kiros, and R. Zemel · 2015
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T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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Imagenet large scale visual recognition challenge
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K. Simonyan and A. Zisserman · 2014
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From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions
P. Young, A. Lai, M. Hodosh, and J. Hockenmaier · 2014
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Edge boxes: Locating object proposals from edges
C. L. Zitnick and P. Dollár · 2014
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Long-term recurrent convolutional networks for visual recognition and description
J. Donahue, L. Anne Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell · 2015
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Grounding of textual phrases in images by reconstruction
A. Rohrbach, M. Rohrbach, R. Hu, T. Darrell, and B. Schiele · 2015
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Show and tell: A neural image caption generator
O. Vinyals, A. Toshev, S. Bengio, and D. Erhan · 2015
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Show, attend and tell: Neural image caption generation with visual attention
K. Xu, J. Ba, R. Kiros, A. Courville, R. Salakhutdinov, R. Zemel, and Y. Bengio · 2015
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Segmentation from natural language expressions
R. Hu, M. Rohrbach, and T. Darrell · 2016
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J. Mao, J. Huang, A. Toshev, O. Camburu, A. Yuille, and K. Murphy · 2016
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