Fine-grained pose prediction, normalization, and recognition

arXiv: Computer Vision and Pattern Recognition, Volume abs/1511.07063, 2015.

Cited by: 34|Bibtex|Views140
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

Pose variation and subtle differences in appearance are key challenges to fine-grained classification. While deep networks have markedly improved general recognition, many approaches to fine-grained recognition rely on anchoring networks to parts for better accuracy. Identifying parts to find correspondence discounts pose variation so tha...More

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