Describing and learning of related parts based on latent structural model in big data

Author:

Liu Lei,Bai Xiao,Zhang Huigang,Zhou Jun,Tang Wenzhong

Funder

NSFC projects

Australian Research Councils DECRA Projects funding scheme

State Key Laboratory of Software Development Environment

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference56 articles.

1. M.K. Hasan, C.J. Pal, Creating a big data resource from the faces of Wikipedia, in: BigVision, 2012.

2. B. Gong, F. Sha, K. Grauman, Overcoming dataset bias: an unsupervised domain adaptation approach, in: NIPS Workshop on Large Scale Visual Recognition and Retrieval, 2012.

3. Z. Wang, R. Liu, Semi-supervised learning for large scale image cosegmentation, in: IEEE International Conference on Computer Vision (ICCV), 2013, pp. 393–400.

4. G. Griffin, A. Holub, P. Perona, Caltech-256 object category dataset. www.vision.caltech.edu/Image_Datasets/Caltech101/.

5. M. Everingham, L. Van Gool, C.K. Williams, J. Winn, A. Zisserman, The PASCAL visual object classes challenge 2010 results, 〈http://www.pascal-network.org/challenges/VOC/voc2010/workshop/index.html〉.

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