SPDA CNN: Unifying Semantic Part Detection and Abstraction for Fine Grained Recognition
Most convolutional neural networks (CNNs) lack midlevel layers that model semantic parts of objects. This limits CNN based methods from reaching their full potential in detecting and utilizing small semantic parts in recognition. Introducing such mid level layers can facilitate the extraction of part specific features which can be utilized for better recognition performance. This is particularly important in the domain of fine grained recognition. In this paper, we propose a new CNN architecture that integrates semantic part detection and abstraction (SPDACNN) for fine grained classification. The proposed network has two sub networks: one for detection and one for recognition. The detection sub network has a novel top down proposal method to generate small semantic part candidates for detection. The classification sub network introduces novel part layers that extract features from parts detected by the detection sub network, and combine them for recognition. As a result, the proposed architecture provides an end to end network that performs detection, localization of multiple semantic parts, and whole object recognition within one framework that shares the computation of convolutional filters. Our method outperforms state of theart methods with a large margin for small parts detection (e.g. our precision of 93.40% vs the best previous precision of 74.00% for detecting the head on CUB 2011) It also compares favorably to the existing state of the art on finegrained classification, e.g. it achieves 85.14% accuracy on CUB 2011.
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)