Mining deep And-Or object structures via cost-sensitive question-answer-based active annotations-Reference-Cited by-同舟云学术

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Mining deep And-Or object structures via cost-sensitive question-answer-based active annotations

Published:2018-11 Issue: Volume:176-177 Page:33-44
ISSN:1077-3142
Container-title:Computer Vision and Image Understanding
language:en
Short-container-title:Computer Vision and Image Understanding

Author:

Zhang Quanshi^ORCID,Wu Ying Nian,Zhang Hao,Zhu Song-Chun

Funder

ONR, United States

Publisher

Elsevier BV

Subject

Computer Vision and Pattern Recognition,Signal Processing,Software

Reference63 articles.

1. Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C.L., Parikh, D., 2015. Vqa: Visual question answering. In: ICCV.

2. Azizpour, H., Laptev, I., 2012. Object detection using strongly-supervised deformable part models. In: ECCV.

3. Ba, J.L., Swersky, K., Fidler, S., Salakhutdinov, R., 2015. Predicting deep zero-shot convolutional neural networks using textual descriptions. In: ICCV.

4. Branson, S., Perona, P., Belongie, S., 2011. Strong supervision from weak annotation: Interactive training of deformable part models. In: ICCV.

5. Caetano, T.S., McAuley, J.J., Cheng, L., Le, Q.V., Smola, A.J., 2007. Learning graph matching. In: ICCV.

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