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Structured output regression for detection with partial truncation

Abstract:

We develop a structured output model for object category detection that explicitly accounts for alignment, multiple aspects and partial truncation in both training and inference. The model is formulated as large margin learning with latent variables and slack rescaling, and both training and inference are computationally efficient. We make the following contributions: (i) we note that extending the Structured Output Regression formulation of Blaschko and Lampert [1] to include a bias term sig...

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Authors


Vedaldi, A More by this author
Zisserman, A More by this author
Journal:
Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference
Pages:
1928-1936
Publication date:
2009
URN:
uuid:1141203e-5718-4380-b532-7791941ea15c
Source identifiers:
321073
Local pid:
pubs:321073
Language:
English

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