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arXiv · 1407.3867

Part-based R-CNNs for Fine-grained Category Detection

Ning Zhang·Jeff Donahue·Ross Girshick·Trevor Darrell
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Abstract

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.

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Ning Zhang, Jeff Donahue, Ross Girshick, Trevor Darrell. 2014-07-15. Part-based R-CNNs for Fine-grained Category Detection. https://arxiv.org/abs/1407.3867

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