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نمایش تعداد 1-10 از 27

OPTIMOL: Automatic Online Picture Collection via Incremental Model Learning

نوع: Journal Paper

نویسنده : Li-Jia Li; Li Fei-Fei

ناشر: Springer

سال: 2010

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Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories

نوع: Journal Paper

نویسنده : Li Fei-Fei; Rob Fergus; Pietro Perona

ناشر: Elsevier Science

سال: 2007

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One-shot learning of object categories

نوع: Journal Paper

نویسنده : Li Fei-Fei; Fergus, R.; Perona, P.

ناشر: IEEE

سال: 2006

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Using Dependent Regions for Object Categorization in a Generative Framework

نوع: Journal Paper

نویسنده : Gang Wang; Ye Zhang; Li Fei-Fei

سال: 2006

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Fine-Grained Crowdsourcing for Fine-Grained Recognition

نوع: Journal Paper

نویسنده : Jia Deng; Jonathan Krause; Li Fei-Fei

سال: 2013

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A codebook-free and annotation-free approach for fine-grained image categorization

نوع: Journal Paper

نویسنده : Bangpeng Yao; G. Bradski; Li Fei-Fei

سال: 2012

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Connectionist Temporal Modeling for Weakly Supervised Action Labeling

نوع: Journal Paper

نویسنده : De-An Huang; Li Fei-Fei; Juan Carlos Niebles

سال: 2016

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Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words

نوع: Journal Paper

نویسنده : Juan Carlos Niebles; Hongcheng Wang; Li Fei-Fei

سال: 2008

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Learning Object Categories From Internet Image Searches

نوع: Journal Paper

نویسنده : Fergus, R.; Li Fei-Fei; Perona, P.; Zisserman, A.

ناشر: IEEE

سال: 2010

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Understanding the 3D layout of a cluttered room from multiple images

نوع: Conference Paper

نویسنده : Bao, S.Y. , Furlan, A. , Li Fei-Fei , Savarese, S.

ناشر: IEEE

سال: 2014

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OPTIMOL: Automatic Online Picture Collection via Incremental Model Learning

Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories

One-shot learning of object categories

Using Dependent Regions for Object Categorization in a Generative Framework

Fine-Grained Crowdsourcing for Fine-Grained Recognition

A codebook-free and annotation-free approach for fine-grained image categorization

Connectionist Temporal Modeling for Weakly Supervised Action Labeling

Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words

Learning Object Categories From Internet Image Searches

Understanding the 3D layout of a cluttered room from multiple images

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OPTIMOL: Automatic Online Picture Collection via Incremental Model Learning

Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories

One-shot learning of object categories

Using Dependent Regions for Object Categorization in a Generative Framework

Fine-Grained Crowdsourcing for Fine-Grained Recognition

A codebook-free and annotation-free approach for fine-grained image categorization

Connectionist Temporal Modeling for Weakly Supervised Action Labeling

Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words

Learning Object Categories From Internet Image Searches

Understanding the 3D layout of a cluttered room from multiple images