CUHK & ETHZ & SIAT Submission to ActivityNet Challenge 2016

Invited paper: Automatic speech recognition: History, methods and challenges

D. O’Shaughnessy · 2008

Earlier work this paper cites.

Image classification with the fisher vector: Theory and practice

J. Sánchez, F. Perronnin, T. Mensink, and J. J. Verbeek · 2013

Earlier work this paper cites.

Action recognition with improved trajectories

H. Wang and C. Schmid · 2013

Earlier work this paper cites.

Two-stream convolutional networks for action recognition in videos

K. Simonyan and A. Zisserman · 2014

Earlier work this paper cites.

Activitynet: A large-scale video benchmark for human activity understanding

B. G. Fabian Caba Heilbron, Victor Escorcia and J. C. Niebles · 2015

Earlier work this paper cites.

Devnet: A deep event network for multimedia event detection and evidence recounting

C. Gan, N. Wang, Y. Yang, D.-Y. Yeung, and A. G. Hauptmann · 2015

Earlier work this paper cites.

Batch normalization: Accelerating deep network training by reducing internal covariate shift

S. Ioffe and C. Szegedy · 2015

Earlier work this paper cites.

Going deeper with convolutions

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015

Cited alongside, same era.

Learning spatiotemporal features with 3d convolutional networks

D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri · 2015

Cited alongside, same era.

Action recognition with trajectory-pooled deep-convolutional descriptors

L. Wang, Y. Qiao, and X. Tang · 2015

Cited alongside, same era.

Fusing multi-stream deep networks for video classification

Original

Z. Wu, Y. Jiang, X. Wang, H. Ye, X. Xue, and J. Wang · 2015

Cited alongside, same era.

Recognize complex events from static images by fusing deep channels

Y. Xiong, K. Zhu, D. Lin, and X. Tang · 2015

Cited alongside, same era.

Deep residual learning for image recognition

Bag of visual words and fusion methods for action recognition: Comprehensive study and good practice

X. Peng, L. Wang, X. Wang, and Y. Qiao · 2016

Closest in time.

Rethinking the inception architecture for computer vision

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016

Closest in time.

Deep convolutional neural networks and data augmentation for acoustic event detection

Original

N. Takahashi, M. Gygli, B. Pfister, and L. Van Gool · 2016

Closest in time.

Mofap: A multi-level representation for action recognition

L. Wang, Y. Qiao, and X. Tang · 2016

Closest in time.

Temporal segment networks: towards good practices for deep action recognition

L. Wang, Y. Xiong, Z. Wang, Y. Qiao, D. Lin, X. Tang, and L. Van Gool · 2016

Closest in time.

Learning multiscale features directly from waveforms