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Although deep neural networks offer better face detection results than shallow or handcrafted models, their complex architectures come with higher computational requirements and slower inference speeds than shallow neural networks.
Chang, C.C., Lin, C.J.: Training ν \nu -Support Vector Regression: Theory and Algorithms. Neural Computation 14, 1959–1977 (2002)
2002
Earlier work this paper cites.
Viola, P., Jones, M.J.: Robust Real-Time Face Detection. International Journal of Computer Vision 57(2), 137–154 (May 2004)
2004
Earlier work this paper cites.
Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of ICML. pp. 41–48 (2009)
2009
Earlier work this paper cites.
Everingham, M., van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The Pascal Visual Object Classes (VOC) Challenge. International Journal of Computer Vision 88(2), 303–338 (Jun 2010)
2010
Earlier work this paper cites.
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object Detection with Discriminatively Trained Part-Based Models. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(9), 1627–1645 (Sep 2010)
2010
Earlier work this paper cites.
Jain, V., Learned-Miller, E.: FDDB: A Benchmark for Face Detection in Unconstrained Settings. Tech. Rep. UM-CS-2010-009, University of Massachusetts, Amherst (2010)
2010
Earlier work this paper cites.
Everingham, M., Van Gool, L., Williams, C., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2012 Results (2012)
2012
Earlier work this paper cites.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of NIPS pp. 1106–1114 (2012)
2012
Earlier work this paper cites.
2012
Earlier work this paper cites.
Ionescu, R.T., Popescu, M., Grozea, C.: Local Learning to Improve Bag of Visual Words Model for Facial Expression Recognition. In: Workshop on Challenges in Representation Learning, ICML (2013)
2013
Earlier work this paper cites.
Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: Delving deep into convolutional nets. In: Proceedings of BMVC (2014)
2014
Earlier work this paper cites.
Everingham, M., Eslami, S.M., Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The Pascal Visual Object Classes Challenge: A Retrospective. International Journal of Computer Vision 111(1), 98–136 (2015)
2015
Cited alongside, same era.
Girshick, R.: Fast R-CNN. In: Proceedings of ICCV. pp. 1440–1448 (2015)
2015
Cited alongside, same era.
Li, H., Lin, Z., Shen, X., Brandt, J., Hua, G.: A convolutional neural network cascade for face detection. In: Proceedings of CVPR. pp. 5325–5334 (2015)
2015
Cited alongside, same era.
Parkhi, O.M., Vedaldi, A., Zisserman, A., et al.: Deep Face Recognition. In: Proceedings of BMVC. pp. 6–17 (2015)
2015
Cited alongside, same era.
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. In: Proceedings of NIPS. pp. 91–99 (2015)
2015
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: SSD: Single Shot MultiBox Detector. In: Proceedings of ECCV. pp. 21–37 (2016)
2016
Later among the works it cites.
Qin, H., Yan, J., Li, X., Hu, X.: Joint Training of Cascaded CNN for Face Detection. In: Proceedings of CVPR. pp. 3456–3465 (2016)
2016
Later among the works it cites.
Yang, S., Luo, P., Loy, C.C., Tang, X.: WIDER FACE: A Face Detection Benchmark. In: Proceedings of CVPR. pp. 5525–5533 (2016)
2016
Later among the works it cites.
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of ICCV. pp. 2961–2969 (2017)
2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., A., K., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (2015)
2015
Cited alongside, same era.
Yang, S., Luo, P., Loy, C.C., Tang, X.: From facial parts responses to face detection: A deep learning approach. In: Proceedings of ICCV. pp. 3676–3684 (2015)
2015
Cited alongside, same era.
Chen, D., Hua, G., Wen, F., Sun, J.: Supervised Transformer Network for Efficient Face Detection. In: Proceedings of ECCV. pp. 122–138 (2016)
2016
Cited alongside, same era.
He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. In: Proceedings of CVPR. pp. 770–778 (2016)
2016
Cited alongside, same era.
Ionescu, R., Alexe, B., Leordeanu, M., Popescu, M., Papadopoulos, D.P., Ferrari, V.: How hard can it be? estimating the difficulty of visual search in an image. In: Proceedings of CVPR. pp. 2157–2166 (2016)
2016
Cited alongside, same era.
2017
Later among the works it cites.
Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Wojna, Z., Song, Y., Guadarrama, S., et al.: Speed/accuracy trade-offs for modern convolutional object detectors. In: Proceedings of CVPR. pp. 7310–7319 (2017)
2017
Later among the works it cites.
Jiang, H., Learned-Miller, E.: Face detection with the Faster R-CNN. In: Proceedings of FG. pp. 650–657 (2017)
2017
Later among the works it cites.
Zhang, S., Zhu, X., Lei, Z., Shi, H., Wang, X., Li, S.Z.: S3̂fd: Single shot scale-invariant face detector. In: Proceedings of ICCV. pp. 192–201 (2017)
2017
Later among the works it cites.
2018
Closest in time.
Soviany, P., Ionescu, R.T.: Optimizing the Trade-off between Single-Stage and Two-Stage Deep Object Detectors using Image Difficulty Prediction. In: Proceedings of SYNASC (2018)
2018
Closest in time.