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C3AE: Exploring the Limits of Compact Model for Age Estimation | | Chao Zhang
; Shuaicheng Liu
; Xun Xu
; Ce Zhu
; | | Date: |
10 Apr 2019 | | Abstract: | Age estimation is a classic learning problem in computer vision. Many larger
and deeper CNNs have been proposed with promising performance, such as AlexNet,
VggNet, GoogLeNet and ResNet. However, these models are not practical for the
embedded/mobile devices. Recently, MobileNets and ShuffleNets have been
proposed to reduce the number of parameters, yielding lightweight models.
However, their representation has been weakened because of the adoption of
depth-wise separable convolution. In this work, we investigate the limits of
compact model for small-scale image and propose an extremely extbf{C}ompact
yet efficient extbf{C}ascade extbf{C}ontext-based extbf{A}ge
extbf{E}stimation model( extbf{C3AE}). This model possesses only 1/9 and
1/2000 parameters compared with MobileNets/ShuffleNets and VggNet, while
achieves competitive performance. In particular, we re-define age estimation
problem by two-points representation, which is implemented by a cascade model.
Moreover, to fully utilize the facial context information, multi-branch CNN
network is proposed to aggregate multi-scale context. Experiments are carried
out on three age estimation datasets. The state-of-the-art performance on
compact model has been achieved with a relatively large margin. | | Source: | arXiv, 1904.5059 | | Services: | Forum | Review | PDF | Favorites | |
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