Fetching the paper…
Reading the bibliography…
Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing.
Knowledge representation for the generation of quantified natural language descriptions of vehicle traffic in image sequences
R. Gerber and H.-H. Nagel · 1996
Earlier work this paper cites.
Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
Earlier work this paper cites.
BLEU: A method for automatic evaluation of machine translation
K. Papineni, S. Roukos, T. Ward, and W. J. Zhu · 2002
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.
Collecting image annotations using amazon’s mechanical turk
C. Rashtchian, P. Young, M. Hodosh, and J. Hockenmaier · 2010
Earlier work this paper cites.
I2t: Image parsing to text description
B. Z. Yao, X. Yang, L. Lin, M. W. Lee, and S.-C. Zhu · 2010
Earlier work this paper cites.
Baby talk: Understanding and generating simple 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.
Composing simple image descriptions using web-scale n-grams
S. Li, G. Kulkarni, T. L. Berg, A. C. Berg, and Y. Choi · 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.
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. C. Berg, K. Yamaguchi, T. L. Berg, K. Stratos, and H. D. III · 2012
Earlier work this paper cites.
Image description using visual dependency representations
D. Elliott and F. Keller · 2013
Cited alongside, same era.
Generating sequences with recurrent neural networks
A. Graves · 2013
Cited alongside, same era.
Framing image description as a ranking task: Data, models and evaluation metrics
M. Hodosh, P. Young, and J. Hockenmaier · 2013
Cited alongside, same era.
Multimodal neural language models
R. Kiros and R. Z. R. Salakhutdinov · 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.
Overfeat: Integrated recognition, localization and detection using convolutional networks
Unifying visual-semantic embeddings with multimodal neural language models
R. Kiros, R. Salakhutdinov, and R. S. Zemel · 2014
Closest in time.
Treetalk: Composition and compression of trees for image descriptions
P. Kuznetsova, V. Ordonez, T. Berg, and Y. Choi · 2014
Closest in time.
Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Closest in time.
Explain images with multimodal recurrent neural networks
J. Mao, W. Xu, Y. Yang, J. Wang, and A. Yuille · 2014
Closest in time.
ImageNet Large Scale Visual Recognition Challenge, 2014
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2014
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun · 2013
Cited alongside, same era.
Neural machine translation by jointly learning to align and translate
D. Bahdanau, K. Cho, and Y. Bengio · 2014
Cited alongside, same era.
Learning phrase representations using RNN encoder-decoder for statistical machine translation
K. Cho, B. van Merrienboer, C. Gulcehre, F. Bougares, H. Schwenk, and Y. Bengio · 2014
Cited alongside, same era.
Decaf: A deep convolutional activation feature for generic visual recognition
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell · 2014
Cited alongside, same era.
Improving image-sentence embeddings using large weakly annotated photo collections
Y. Gong, L. Wang, M. Hodosh, J. Hockenmaier, and S. Lazebnik · 2014
Cited alongside, same era.
Deep fragment embeddings for bidirectional image sentence mapping
A. Karpathy, A. Joulin, and L. Fei-Fei · 2014
Cited alongside, same era.
Closest in time.
Grounded compositional semantics for finding and describing images with sentences
R. Socher, A. Karpathy, Q. V. Le, C. Manning, and A. Y. Ng · 2014
Closest in time.
Sequence to sequence learning with neural networks
I. Sutskever, O. Vinyals, and Q. V. Le · 2014
Closest in time.
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
Closest in time.
Recurrent neural network regularization
W. Zaremba, I. Sutskever, and O. Vinyals · 2014
Closest in time.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Closest in time.
CIDEr: Consensus-based image description evaluation
R. Vedantam, C. L. Zitnick, and D. Parikh · 2015
Closest in time.