Abstract
We present a label-based, semantic distance induced regularization learning method for Fine-grained image recognition (FGIR). In contrast to previous label-based methods that involve a nontrivial optimization in multi-task metric learning, our approach can be integrated into an end-to-end network without introducing any extra parameters, thus easy to be optimized. To this end, a category-level hierarchical distance matrix (HDM) that encodes semantic distance between subcategories through a tree-like label hierarchy is constructed. HDM is then incorporated into a DCNN to aggregate misclassified prediction probabilities for model learning, thus providing additional discriminative information for fine-grained feature learning. Experiments on three fine-grained benchmark datasets (Stanford Cars, FGVC-Aircraft, CUB-Birds) validate the effectiveness of our approach and demonstrate its improvements over previous methods.
The first author is a student.
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Acknowledgement
This work was supported in part by the National Natural Science Foundation of China under Grant 61702197, in part by the Natural Science Foundation of Guangdong Province under Grant 2017A030310261, in part by the program of China Scholarship Council.
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Mo, X., Zhu, J., Zhao, X., Liu, M., Wei, T., Luo, W. (2019). Exploiting Category-Level Semantic Relationships for Fine-Grained Image Recognition. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11857. Springer, Cham. https://doi.org/10.1007/978-3-030-31654-9_5
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