Multi-Objective Convolutional Learning for Face Labeling
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| Authors | Sifei Liu, Jimei Yang, Chang Huang, Ming-Hsuan Yang |
| Journal/Conference Name | IEEE Conference on Computer Vision and Pattern… |
| Paper Category | ECE |
| Paper Abstract | This paper formulates face labeling as a conditional random field with unary and pairwise classifiers. We develop a novel multi-objective learning method that optimizes a single unified deep convolutional network with two distinct non-structured loss functions: one encoding the unary label likelihoods and the other encoding the pairwise label dependencies. Moreover, we regularize the network by using a nonparametric prior as new input channels in addition to the RGB image, and show that significant performance improvements can be achieved with a much smaller network size. Experiments on both the LFW and Helen datasets demonstrate state-of-the-art results of the proposed algorithm, and accurate labeling results on challenging images can be obtained by the proposed algorithm for real-world applications. |
| Date of publication | 2015 |
| Code Programming Language | Python |
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