Deep LAC: Deep localization, alignment and classification for fine-grained recognition

We propose a fine-grained recognition system that incorporates part localization, alignment, and classification in one deep neural network. This is a nontrivial process, as the input to the classification module should be functions that enable back-propagation in constructing the solver. Our major c...

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Bibliographic Details
Published in2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 1666 - 1674
Main Authors Di Lin, Xiaoyong Shen, Cewu Lu, Jiaya Jia
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2015
Subjects
Birds
Couplings
Feature extraction
Head
Neural networks
Training
Valves
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Summary:We propose a fine-grained recognition system that incorporates part localization, alignment, and classification in one deep neural network. This is a nontrivial process, as the input to the classification module should be functions that enable back-propagation in constructing the solver. Our major contribution is to propose a valve linkage function (VLF) for back-propagation chaining and form our deep localization, alignment and classification (LAC) system. The VLF can adaptively compromise the errors of classification and alignment when training the LAC model. It in turn helps update localization. The performance on fine-grained object data bears out the effectiveness of our LAC system.
ISSN:1063-6919
DOI:10.1109/CVPR.2015.7298775