Fetching the paper…
Reading the bibliography…
In this paper, we introduce a new dataset consisting of 360,001 focused natural language descriptions for 10,738 images.
Generating typed dependency parses from phrase structure parses
M.-C. De Marneffe, B. MacCartney, C. D. Manning, et al · 2006
Earlier work this paper cites.
Generating image descriptions using dependency relational patterns
A. Aker and R. Gaizauskas · 2010
Earlier work this paper cites.
Every picture tells a story: Generating sentences from images
A. Farhadi, M. Hejrati, M. A. Sadeghi, P. Young, C. Rashtchian, J. Hockenmaier, and D. Forsyth · 2010
Earlier work this paper cites.
Topic models for image annotation and text illustration
Y. Feng and M. Lapata · 2010
Earlier work this paper cites.
Collecting image annotations using amazon’s mechanical turk
C. Rashtchian, P. Young, M. Hodosh, and J. Hockenmaier · 2010
Earlier work this paper cites.
Natural language processing (almost) from scratch
R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa · 2011
Earlier work this paper cites.
Baby talk: Understanding and generating image descriptions
G. Kulkarni, V. Premraj, S. Dhar, S. Li, Y. Choi, A. C. Berg, and T. L. Berg · 2011
Earlier work this paper cites.
Im2text: Describing images using 1 million captioned photographs
V. Ordonez, G. Kulkarni, and T. L. Berg · 2011
Earlier work this paper cites.
Corpus-guided sentence generation of natural images
Y. Yang, C. L. Teo, H. Daumé III, and Y. Aloimonos · 2011
Earlier work this paper cites.
Understanding and predicting importance in images
A. C. Berg, T. L. Berg, H. D. III, J. Dodge, A. Goyal, X. Han, A. Mensch, M. Mitchell, A. Sood, K. Stratos, and K. Yamaguchi · 2012
Earlier work this paper cites.
Collective generation of natural image descriptions
P. Kuznetsova, V. Ordonez, A. C. Berg, T. L. Berg, and Y. Choi · 2012
Earlier work this paper cites.
Midge: Generating image descriptions from computer vision detections
M. Mitchell, X. Han, J. Dodge, A. Mensch, A. Goyal, A. Berg, K. Yamaguchi, T. Berg, K. Stratos, and H. Daumé III · 2012
Earlier work this paper cites.
Generating natural-language video descriptions using text-mined knowledge
N. Krishnamoorthy, G. Malkarnenkar, R. J. Mooney, K. Saenko, and S. Guadarrama · 2013
Cited alongside, same era.
Domain-independent captioning of domain-specific images
R. Mason · 2013
Cited alongside, same era.
Efficient estimation of word representations in vector space
T. Mikolov, K. Chen, G. Corrado, and J. Dean · 2013
Cited alongside, same era.
Question answering with subgraph embeddings
A. Bordes, S. Chopra, and J. Weston · 2014
Cited alongside, same era.
Open question answering with weakly supervised embedding models
A. Bordes, J. Weston, and N. Usunier · 2014
Cited alongside, same era.
Unifying visual-semantic embeddings with multimodal neural language models
R. Kiros, R. Salakhutdinov, and R. S. Zemel · 2014
Later among the works it cites.
Microsoft COCO: common objects in context
T. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Later among the works it cites.
A multi-world approach to question answering about real-world scenes based on uncertain input
M. Malinowski and M. Fritz · 2014
Later among the works it cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Later among the works it cites.
Translating videos to natural language using deep recurrent neural networks
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
X. Chen and C. L. Zitnick · 2014
Cited alongside, same era.
Long-term recurrent convolutional networks for visual recognition and description
J. Donahue, L. A. Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell · 2014
Cited alongside, same era.
From captions to visual concepts and back
H. Fang, S. Gupta, F. Iandola, R. Srivastava, L. Deng, P. Dollár, J. Gao, X. He, M. Mitchell, J. Platt, et al · 2014
Cited alongside, same era.
Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
Cited alongside, same era.
A multi-view embedding space for modeling internet images, tags, and their semantics
Y. Gong, Q. Ke, M. Isard, and S. Lazebnik · 2014
Cited alongside, same era.
Deep visual-semantic alignments for generating image descriptions
A. Karpathy and L. Fei-Fei · 2014
Cited alongside, same era.
S. Venugopalan, H. Xu, J. Donahue, M. Rohrbach, R. Mooney, and K. Saenko · 2014
Later among the works it cites.
Show and tell: A neural image caption generator
O. Vinyals, A. Toshev, S. Bengio, and D. Erhan · 2014
Later among the works it cites.
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
Later among the works it cites.
Visual turing test for computer vision systems
D. Geman, S. Geman, N. Hallonquist, and L. Younes · 2015
Closest in time.
R. Lebret, P. O. Pinheiro, and R. Collobert · 2015
Closest in time.
Don’t just listen, use your imagination: Leveraging visual common sense for non-visual tasks
X. Lin and D. Parikh · 2015
Closest in time.
Weakly supervised memory networks
S. Sukhbaatar, A. Szlam, J. Weston, and R. Fergus · 2015
Closest in time.