Semantic part localization can facilitate fine-grained categorization by explicitly isolating subtle appearance differences associated with specific object parts. Methods for pose-normalized representations have been proposed, but generally presume bounding box annotations at test time due to the difficulty of object detection. We propose a model for fine-grained categorization that overcomes these limitations by leveraging deep convolutional features computed on bottom-up region proposals. Our method learns whole-object and part detectors, enforces learned geometric constraints between them, and predicts a fine-grained category from a pose-normalized representation. Experiments on the Caltech-UCSD bird dataset confirm that our method outperforms state-of-the-art fine-grained categorization methods in an end-to-end evaluation without requiring a bounding box at test time. © 2014 Springer International Publishing.
CITATION STYLE
Zhang, N., Donahue, J., Girshick, R., & Darrell, T. (2014). Part-based R-CNNs for fine-grained category detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8689 LNCS, pp. 834–849). Springer Verlag. https://doi.org/10.1007/978-3-319-10590-1_54
Mendeley helps you to discover research relevant for your work.