Built on

Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
Earlier work this paper cites.
Histograms of oriented optical flow and binet-cauchy kernels on nonlinear dynamical systems for the recognition of human actions
R. Chaudhry, A. Ravichandran, G. Hager, and R. Vidal · 2009
Earlier work this paper cites.
HMDB: a large video database for human motion recognition
H. Kuehne, H. Jhuang, E. Garrote, T. Poggio, and T. Serre · 2011
Earlier work this paper cites.
UCF101: A dataset of 101 human actions classes from videos in the wild
Original
K. Soomro, A. R. Zamir, and M. Shah · 2012
Earlier work this paper cites.
Action recognition with improved trajectories
H. Wang and C. Schmid · 2013
Earlier work this paper cites.
Large-Scale Object Classification Using Label Relation Graphs
J. Deng, N. Ding, Y. Jia, A. Frome, K. Murphy, S. Bengio, Y. Li, H. Neven, and H. Adam · 2014
Earlier work this paper cites.
Long-term recurrent convolutional networks for visual recognition and description
Original
J. Donahue, L. A. Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell · 2014
Earlier work this paper cites.
Caffe: Convolutional architecture for fast feature embedding
Original
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
Earlier work this paper cites.
Large-scale video classification with convolutional neural networks
A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei · 2014
Earlier work this paper cites.

Similar

Large-scale video classification with convolutional neural networks
A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Original
D. P. Kingma and J. Ba · 2014
Cited alongside, same era.
Imagenet large scale visual recognition challenge
Original
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. S. Bernstein, A. C. Berg, and F. Li · 2014
Cited alongside, same era.
Sequence to sequence learning with neural networks
Original
I. Sutskever, O. Vinyals, and Q. V. Le · 2014
Cited alongside, same era.
Translating videos to natural language using deep recurrent neural networks
Original

Then

Structural-rnn: Deep learning on spatio-temporal graphs
Original
A. Jain, A. R. Zamir, S. Savarese, and A. Saxena · 2015
Later among the works it cites.
Beyond short snippets: Deep networks for video classification
Original
J. Y. Ng, M. J. Hausknecht, S. Vijayanarasimhan, O. Vinyals, R. Monga, and G. Toderici · 2015
Later among the works it cites.
Rethinking the inception architecture for computer vision
Original
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2015
Later among the works it cites.
Movieqa: Understanding stories in movies through question-answering
Original
M. Tapaswi, Y. Zhu, R. Stiefelhagen, A. Torralba, R. Urtasun, and S. Fidler · 2015
Later among the works it cites.
Youtube-8m: A large-scale video classification benchmark

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…

2017

Hierarchical Label Inference for Video Classification

Nauata, Nelson, Smith, Jonathan, Mori, Greg

Understand

Videos are a rich source of high-dimensional structured data, with a wide range of interacting components at varying levels of granularity.

In order to improve understanding of unconstrained internet videos, it is important to consider the role of labels at separate levels of abstraction.
In this paper, we consider the use of the Bidirectional Inference Neural Network (BINN) for performing graph-based inference in label space for the task of video classification.
We take advantage of the inherent hierarchy between labels at increasing granularity.

Built on

Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
Earlier work this paper cites.
Histograms of oriented optical flow and binet-cauchy kernels on nonlinear dynamical systems for the recognition of human actions
R. Chaudhry, A. Ravichandran, G. Hager, and R. Vidal · 2009
Earlier work this paper cites.
HMDB: a large video database for human motion recognition
H. Kuehne, H. Jhuang, E. Garrote, T. Poggio, and T. Serre · 2011
Earlier work this paper cites.
UCF101: A dataset of 101 human actions classes from videos in the wild
Original
K. Soomro, A. R. Zamir, and M. Shah · 2012
Earlier work this paper cites.
Action recognition with improved trajectories
H. Wang and C. Schmid · 2013
Earlier work this paper cites.
Large-Scale Object Classification Using Label Relation Graphs
J. Deng, N. Ding, Y. Jia, A. Frome, K. Murphy, S. Bengio, Y. Li, H. Neven, and H. Adam · 2014
Earlier work this paper cites.
Long-term recurrent convolutional networks for visual recognition and description
Original
J. Donahue, L. A. Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell · 2014
Earlier work this paper cites.
Caffe: Convolutional architecture for fast feature embedding
Original
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
Earlier work this paper cites.
Large-scale video classification with convolutional neural networks
A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei · 2014
Earlier work this paper cites.

Similar

Large-scale video classification with convolutional neural networks
A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Original
D. P. Kingma and J. Ba · 2014
Cited alongside, same era.
Imagenet large scale visual recognition challenge
Original
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. S. Bernstein, A. C. Berg, and F. Li · 2014
Cited alongside, same era.
Sequence to sequence learning with neural networks
Original
I. Sutskever, O. Vinyals, and Q. V. Le · 2014
Cited alongside, same era.
Translating videos to natural language using deep recurrent neural networks
Original

Then

Structural-rnn: Deep learning on spatio-temporal graphs
Original
A. Jain, A. R. Zamir, S. Savarese, and A. Saxena · 2015
Later among the works it cites.
Beyond short snippets: Deep networks for video classification
Original
J. Y. Ng, M. J. Hausknecht, S. Vijayanarasimhan, O. Vinyals, R. Monga, and G. Toderici · 2015
Later among the works it cites.
Rethinking the inception architecture for computer vision
Original
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2015
Later among the works it cites.
Movieqa: Understanding stories in movies through question-answering
Original
M. Tapaswi, Y. Zhu, R. Stiefelhagen, A. Torralba, R. Urtasun, and S. Fidler · 2015
Later among the works it cites.
Youtube-8m: A large-scale video classification benchmark

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

S. Venugopalan, H. Xu, J. Donahue, M. Rohrbach, R. J. Mooney, and K. Saenko · 2014

Cited alongside, same era.

Structure inference machines: Recurrent neural networks for analyzing relations in group activity recognition

Original

Z. Deng, A. Vahdat, H. Hu, and G. Mori · 2015

Cited alongside, same era.

Probabilistic label relation graphs with ising models

Original

N. Ding, J. Deng, K. Murphy, and H. Neven · 2015

Cited alongside, same era.

Learning structured inference neural networks with label relations

Original

H. Hu, G. Zhou, Z. Deng, Z. Liao, and G. Mori · 2015

Cited alongside, same era.

Original

S. Abu-El-Haija, N. Kothari, J. Lee, P. Natsev, G. Toderici, B. Varadarajan, and S. Vijayanarasimhan · 2016

Later among the works it cites.

Hierarchical deep temporal models for group activity recognition

Original

M. S. Ibrahim, S. Muralidharan, Z. Deng, A. Vahdat, and G. Mori · 2016

Later among the works it cites.

Cached long short-term memory neural networks for document-level sentiment classification

Original

J. Xu, D. Chen, X. Qiu, and X. Huang · 2016

Later among the works it cites.

Cnn architectures for large-scale audio classification

S. Hershey, S. Chaudhuri, D. P. W. Ellis, J. F. Gemmeke, A. Jansen, C. Moore, M. Plakal, D. Platt, R. A. Saurous, B. Seybold, M. Slaney, R. Weiss, and K. Wilson · 2017

Closest in time.