By Enqing Chen, Yihong Gong, Yun Tie
The two-volume lawsuits LNCS 9916 and 9917, represent the court cases of the 17th Pacific-Rim convention on Multimedia, PCM 2016, held in Xi`an, China, in September 2016.
The overall of 128 papers provided in those complaints used to be rigorously reviewed and chosen from 202 submissions.
The concentration of the convention was once as follows in multimedia content material research, multimedia sign processing and communications, and multimedia functions and services.
Read Online or Download Advances in Multimedia Information Processing - PCM 2016: 17th Pacific-Rim Conference on Multimedia, Xi´ an, China, September 15-16, 2016, Proceedings, Part I PDF
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Extra resources for Advances in Multimedia Information Processing - PCM 2016: 17th Pacific-Rim Conference on Multimedia, Xi´ an, China, September 15-16, 2016, Proceedings, Part I
Be updated by Δwconv5 = −r ∂w conv5 The parameters in sub-network layers are only related to the sub-network loss. , conv4, the contour sub-network loss and the inner sub-network loss are back propagate together, can be represented as Δw = −r ∂Einn ∂Econ −λ∗r ∂w ∂w (3) where Econ is the loss of contour sub-network, Einn is the loss of inner subnetwork, λ is the weight parameter to balance contour sub-network loss and inner sub-network loss, we choose 2 for our experiments. 26 3 K. He and X. Xue Experiments We conduct experiments on the 300-W dataset .
The template corresponding to the interval which r falls into will be eliminated and then a new template will be added to the end of the template set. In this way, it can be guaranteed that the old templates will be updated slowly while the new ones update at a faster speed. It is notable that the template sets will only be updated every k2 (k2 < k1 ) frames and the substitute templates are the same as those in step1 which is the corresponding tracking results of p. On Combining Compressed Sensing and Sparse Representations 4 39 Experimental Results and Analyses In this section, the tracking framework mentioned in this paper will be compared with the 8 prevalent tracking algorithms in 10 standardized video sequences.
1007/978-3-319-10605-2 1 18. : Facial landmark detection by deep multitask learning. , Tuytelaars, T. ) ECCV 2014. LNCS, vol. 8693, pp. 94–108. Springer, Heidelberg (2014). 1007/ 978-3-319-10599-4 7 19. : Extensive facial landmark localization with coarse-to-ﬁne convolutional network cascade. In: 2013 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 386–391. IEEE (2013) 20. : Face detection, pose estimation, and landmark localization in the wild. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.
Advances in Multimedia Information Processing - PCM 2016: 17th Pacific-Rim Conference on Multimedia, Xi´ an, China, September 15-16, 2016, Proceedings, Part I by Enqing Chen, Yihong Gong, Yun Tie