Abstract
Many computer vision tasks are template-based learning tasks in which multiple instances of a specific concept (e.g. multiple images of a subject’s face) are available at once to the learning algorithm. The template structure of the input data provides an opportunity for generating a robust and discriminative unified template-level representation that effectively exploits the inherent diversity of feature-level information across instances within a template. In contrast to other feature aggregation methods, we propose a new technique to dynamically predict weights that consider factors such as noise and redundancy in assessing the importance of image-level features and use those weights to appropriately aggregate the features into a single template-level representation. We present extensive experimental results on the MNIST, CIFAR10, UCF101, IJB-A, IJB-B, and Janus CS4 datasets to show that the new technique outperforms statistical feature pooling methods as well as other neural-network-based aggregation mechanisms on a broad set of tasks.
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References
Wolf, L., Hassner, T., Maoz, I.: Face recognition in unconstrained videos with matched background similarity. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 529–534 (2011)
Klare, B.F., et al.: Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1931–1939 (2015)
Whitelam, C., et al.: IARPA Janus Benchmark-B face dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Ma, L., Lu, J., Feng, J., Zhou, J.: Multiple feature fusion via weighted entropy for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3128–3136 (2015)
Hassner, T., et al.: Pooling faces: template based face recognition with pooled face images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 59–67 (2016)
Kawulok, M., Celebi, E., Smolka, B.: Advances in Face Detection and Facial Image Analysis. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-25958-1
Feichtenhofer, C., Pinz, A., Zisserman, A.: Convolutional two-stream network fusion for video action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1933–1941 (2016)
Yang, J., Ren, P., Chen, D., Wen, F., Li, H., Hua, G.: Neural aggregation network for video face recognition. arXiv preprint arXiv:1603.05474 (2016)
Liao, Q., Leibo, J.Z., Poggio, T.: Learning invariant representations and applications to face verification. In: Advances in Neural Information Processing Systems, pp. 3057–3065 (2013)
Pal, D.K., Juefei-Xu, F., Savvides, M.: Discriminative invariant kernel features: a bells-and-whistles-free approach to unsupervised face recognition and pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5590–5599 (2016)
Luong, M.T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025 (2015)
Wang, P., Cao, Y., Shen, C., Liu, L., Shen, H.T.: Temporal pyramid pooling based convolutional neural networks for action recognition. arXiv preprint arXiv:1503.01224 (2015)
Sharma, S., Kiros, R., Salakhutdinov, R.: Action recognition using visual attention. arXiv preprint arXiv:1511.04119 (2015)
Alkinani, M.H., El-Sakka, M.R.: Patch-based models and algorithms for image denoising: a comparative review between patch-based images denoising methods for additive noise reduction. EURASIP J. Image Video Process. 2017, 58 (2017)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998)
AbdAlmageed, W., et al.: Face recognition using deep multi-pose representations. In: Proceedings of the IEEE Winter Conference on Applications of Computer Vision, pp. 1–9 (2016)
Liu, S., Deng, W.: Very deep convolutional neural network based image classification using small training sample size. In: Proceedings of the IEEE Asian Conference on Pattern Recognition, pp. 730–734 (2015)
Masi, I., Tran, A.T., Hassner, T., Leksut, J.T., Medioni, G.: Do we really need to collect millions of faces for effective face recognition? In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 579–596. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46454-1_35
Crosswhite, N., Byrne, J., Stauffer, C., Parkhi, O., Cao, Q., Zisserman, A.: Template adaptation for face verification and identification. In: Proceedings of the IEEE International Conference on Automatic Face & Gesture Recognition, pp. 1–8. IEEE (2017)
Parkhi, O.M., Vedaldi, A., Zisserman, A., et al.: Deep face recognition. In: Proceedings of the British Machine Vision Conference, vol. 1, p. 6 (2015)
Sankaranarayanan, S., Alavi, A., Castillo, C.D., Chellappa, R.: Triplet probabilistic embedding for face verification and clustering. In: Proceedings of the IEEE International Conference on Biometrics Theory, Applications and Systems, pp. 1–8 (2016)
Masi, I., Hassner, T., Tran, A.T., Medioni, G.: Rapid synthesis of massive face sets for improved face recognition. In: Proceedings of the IEEE Automatic Face & Gesture Recognition, pp. 604–611 (2017)
Guo, Y., Zhang, L., Hu, Y., He, X., Gao, J.: MS-Celeb-1M: a dataset and benchmark for large-scale face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 87–102. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_6
Yi, D., Lei, Z., Liao, S., Li, S.Z.: Learning face representation from scratch. arXiv preprint arXiv:1411.7923 (2014)
Soomro, K., Zamir, A.R., Shah, M.: Ucf101: a dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402 (2012)
Carreira, J., Zisserman, A.: Quo vadis, action recognition? A new model and the kinetics dataset. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, pp. 4724–4733 (2017)
Pérez, J.S., Meinhardt-Llopis, E., Facciolo, G.: Tv-l1 optical flow estimation. Image Process. On Line 2013, 137–150 (2013)
Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: Advances in Neural Information Processing Systems, pp. 568–576 (2014)
Wang, L., et al.: Temporal segment networks: towards good practices for deep action recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 20–36. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_2
Zhu, Y., Lan, Z., Newsam, S., Hauptmann, A.G.: Hidden two-stream convolutional networks for action recognition. arXiv preprint arXiv:1704.00389 (2017)
Acknowledgments
This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via IARPA 2014-14071600011. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purpose notwithstanding any copyright annotation thereon.
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Li, Z., Wu, Y., Abd-Almageed, W., Natarajan, P. (2019). Weighted Feature Pooling Network in Template-Based Recognition. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11365. Springer, Cham. https://doi.org/10.1007/978-3-030-20873-8_28
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DOI: https://doi.org/10.1007/978-3-030-20873-8_28
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