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.
Deep LAC REF proposes a valve linkage function for backpropagation chaining, forming a deep localization, alignment and classification system.
Deep LAC: Deep localization, alignment and classification for fine-grained recognition
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science