Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/77673
Title: Higher-order integration of hierarchical convolutional activations for fine-grained visual categorization
Authors: Cai, S 
Zuo, W
Zhang, L 
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers
Source: Proceedings of the IEEE International Conference on Computer Vision, 2017, 22-29 Oct. 2017, 8237325, p. 511-520 How to cite?
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 issues, we propose an end-to-end framework based on higherorder 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 interactions, 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 hypercolumns simply concatenate maps from different layers, and holistically-nested network 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.
URI: http://hdl.handle.net/10397/77673
ISBN: 9.78154E+12
DOI: 10.1109/ICCV.2017.63
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