The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
A discriminative CNN video representation for event detection
Open accessAbstract
In this paper, we propose a discriminative video representation for event detection over a large scale video dataset when only limited hardware resources are available. The focus of this paper is to effectively leverage deep Convolutional Neural Networks (CNNs) to advance event detection, where only frame level static descriptors can be extracted by the existing CNN toolkits. This paper makes two contributions to the inference of CNN video representation. First, while average pooling and max pooling have long been the standard approaches to aggregating frame level static features, we show that performance can be significantly improved by taking advantage of an appropriate encoding method. Second, we propose using a set of latent concept descriptors as the frame descriptor, which enriches visual information while keeping it computationally affordable. The integration of the two contributions results in a new state-of-the-art performance in event detection over the largest video datasets. Compared to improved Dense Trajectories, which has been recognized as the best video representation for event detection, our new representation improves the Mean Average Precision (mAP) from 27.6% to 36.8% for the TRECVID MEDTest 14 dataset and from 34.0% to 44.6% for the TRECVID MEDTest 13 dataset.
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Cites
ImageNet classification with deep convolutional neural networks
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Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
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Large-Scale Video Classification with Convolutional Neural Networks
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.223 · Cited by 6,354
Action Recognition with Improved Trajectories
Wang, H., & Schmid, C. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.441 · Cited by 3,407
Product Quantization for Nearest Neighbor Search
Jégou, H., Douze, M., & Schmid, C. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.57 · Cited by 2,956 · cited 6×
“In addition, we propose to use Product Quantization [15] based on CNN video representation to speed up the execution (event search) time.”
MethodInfluentialImage Classification with the Fisher Vector: Theory and Practice
Sánchez, J., Perronnin, F., Mensink, T., & Verbeek, J. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0636-x · Cited by 1,498
Cited by
CNN: Single-label to Multi-label
Wei, Y., Xia, W., Huang, J., Ni, B., Dong, J., Zhao, Y., & Yan, S. (2014) · arXiv · doi:10.48550/arxiv.1406.5726 · Cited by 784
Yang, F., Choi, W., & Lin, Y. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.234 · Cited by 608
Objects2action: Classifying and Localizing Actions without Any Video Example
Jain, M., Gemert, J. C. V., Mensink, T., & Snoek, C. G. M. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.521 · Cited by 142
Near-Duplicate Video Retrieval by Aggregating Intermediate CNN Layers
Kordopatis-Zilos, G., Papadopoulos, S., Patras, I., & Kompatsiaris, Y. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-51811-4_21 · Cited by 83
Body Joint Guided 3-D Deep Convolutional Descriptors for Action Recognition
Cao, C., Zhang, Y., Zhang, C., & Lu, H. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2756840 · Cited by 78
Video Emotion Recognition with Transferred Deep Feature Encodings
Xu, B., Fu, Y., Jiang, Y.-G., Li, B., & Sigal, L. (2016) · Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval · doi:10.1145/2911996.2912006 · Cited by 53
Event-based media processing and analysis: A survey of the literature
Tzelepis, C., Ma, Z., Mezaris, V., Ionescu, B., Kompatsiaris, I., Boato, G., Sebe, N., & Yan, S. (2016) · Image and Vision Computing · doi:10.1016/j.imavis.2016.05.005 · Cited by 49
Deep Learning for Video Classification: A Review
Rehman, A., & Belhaouari, S. B. (2021) · doi:10.36227/techrxiv.15172920 · Cited by 17
Metadata
- Authors
- Xu, Z., Yang, Y., & Hauptmann, A. G.
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 1798-1807
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1950136256
- Cited by
- 435
- References
- 9
- FWCI
- 36.58
- Type
- conference paper
- Language
- en
- Open access
- green
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