Higher-order Integration of Hierarchical Convolutional Activations for Fine-grained Visual Categorization

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Authors S. Cai, W. Zuo, L. Zhang
Journal/Conference Name 2017 International Conference on Computer Vision (ICCV 2017)
Paper Category
Paper Abstract The success of fine-grained visual categorization (FGVC) extremely relies on the modeling of appearance and interactions of various semantic parts. This makes FGVC very challenging because: (i) part annotation and detection require expert guidance and are very expensive; (ii) parts are of different sizes; and (iii) the part interactions are complex and of higher-order. To address these is-sues, we propose an end-to-end framework based on higher-order integration of hierarchical convolutional activations for FGVC. By treating the convolutional activations as local descriptors, hierarchical convolutional activations can serve as a representation of local parts from different scales. A polynomial kernel based predictor is proposed to capture higher-order statistics of convolutional activations for modeling part interaction. To model inter-layer part inter-actions, we extend polynomial predictor to integrate hierarchical activations via kernel fusion. Our work also provides a new perspective for combining convolutional activations from multiple layers. While hyper columns simply concatenate maps from different layers, and holistically-nested net-work uses weighted fusion to combine side-outputs, our approach exploits higher-order intra-layer and inter-layer relations for better integration of hierarchical convolutional features. The proposed framework yields more discriminative representation and achieves competitive results on the widely used FGVC datasets.
Date of publication 2017
Code Programming Language MATLAB
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