mlsanityData to 7 Oct 2026

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

arXiv 1407.3867 ↗First posted 15 Jul 2014cs.CVcited by 107

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Abstract (arXiv)

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.

Key facts. Cited by 107 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Jul 2014.

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Citing papers per year: 2021: 30; 2022: 23; 2023: 18; 2024: 15; 2025: 8; 2026: 13.

107 papers.

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50 most cited of 107 All 107 (JSON)