S. Andrews, I. Tsochantaridis, and T. Hofmann, Support vector machines for multiple-instance learning, NIPS, vol.1, 2003.

Y. Bengio, J. Louradour, R. Collobert, and J. Weston, Curriculum learning, ICML, 2009.

H. Bilen, M. Pedersoli, and T. Tuytelaars, Weakly supervised object detection with posterior regularization, BMVC, 2014.

H. Bilen and A. Vedaldi, Weakly supervised deep detection networks, CVPR, vol.7, 2006.

O. Chapelle, B. Schölkopf, and A. Zien, Semi-Supervised Learning, vol.1, p.3, 2006.

M. Choi, J. Park, J. Jung, H. Jung, J. Lee et al., Co-occurrence matrix analysis-based semi-supervised training for object detection, vol.2, p.3, 2018.

X. Dong, L. Zheng, F. Ma, Y. Yang, and D. Meng, Fewexample object detection with model communication, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.41, issue.7, pp.1641-1654, 2018.

M. Everingham, L. Van-gool, C. K. Williams, J. Winn, and A. Zisserman, The pascal visual object classes (voc) challenge, International journal of computer vision, vol.88, issue.2, pp.303-338, 2010.

Q. Fan, W. Zhuo, and Y. Tai, Few-shot object detection with attention-RPN and multi-relation detector, 2019.

M. Gao, A. Li, R. Yu, V. I. Morariu, and L. S. Davis, C-wsl: Count-guided weakly supervised localization, In ECCV, issue.2, 2018.

R. Girshick, Fast r-cnn, ICCV, 2015.

R. Cinbis, J. Verbeek, and C. Schmid, Multi-fold mil training for weakly supervised object localization, CVPR, vol.1, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00975746

S. Guo, W. Huang, H. Zhang, C. Zhuang, D. Dong et al., Curriculumnet: Weakly supervised learning from large-scale web images, ECCV, p.6, 2003.

B. Hariharan and R. B. Girshick, Low-shot visual recognition by shrinking and hallucinating features, 2017.

K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, CVPR, 2016.

G. Hinton, O. Vinyals, and J. Dean, Distilling the knowledge in a neural network, 2015.

E. Hoffer and N. Ailon, Semi-supervised deep learning by metric embedding, 2016.

J. Hoffman, S. Guadarrama, E. S. Tzeng, R. Hu, J. Donahue et al., Lsda: Large scale detection through adaptation, NIPS, vol.1, p.3, 2014.

J. Hoffman, D. Pathak, T. Darrell, and K. Saenko, Detector discovery in the wild: Joint multiple instance and representation learning, CVPR, vol.1, p.3, 2015.

A. Iscen, G. Tolias, Y. Avrithis, and O. Chum, Label propagation for deep semi-supervised learning, CVPR, pp.5070-5079, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02370207

B. Kang, Z. Liu, X. Wang, F. Yu, J. Feng et al., Few-shot object detection via feature reweighting, ICCV, 2003.

S. Laine and T. Aila, Temporal ensembling for semisupervised learning, ICLR, vol.2, p.4, 2017.

D. Lee, Pseudo-label: The simple and efficient semisupervised learning method for deep neural networks, ICMLW, vol.3, 2013.

Y. Li, J. Yang, Y. Song, L. Cao, J. Luo et al., Learning from noisy labels with distillation, 2017.

Z. Li and D. Hoiem, Learning without forgetting, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.40, issue.12, pp.2935-2947, 2003.

X. Liang, S. Liu, Y. Wei, L. Liu, L. Lin et al., Towards computational baby learning: A weakly-supervised approach for object detection, CVPR, vol.3, p.6, 2015.

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed et al., Ssd: Single shot multibox detector, ECCV, 2016.

K. Nguyen and S. Todorovic, Feature weighting and boosting for few-shot segmentation, ICCV, 2003.

D. P. Papadopoulos, J. R. Uijlings, F. Keller, and V. Ferrari, Training object class detectors with click supervision, CVPR, pp.6374-6383, 2017.

N. Papernot, P. Mcdaniel, X. Wu, S. Jha, and A. Swami, Distillation as a defense to adversarial perturbations against deep neural networks, 2015.

I. Radosavovic, P. Dollar, R. Girshick, G. Gkioxari, and K. He, Data distillation: Towards omni-supervised learning, CVPR, vol.2, p.3, 2018.

K. Rakelly, E. Shelhamer, T. Darrell, A. A. Efros, and S. Levine, Few-shot segmentation propagation with guided networks, 2018.

A. Rasmus, M. Berglund, M. Honkala, H. Valpola, and T. Raiko, Semi-supervised learning with ladder networks, NIPS, vol.2, p.4, 2015.

J. Redmon and A. Farhadi, Yolo9000: better, faster, stronger, CVPR, 2017.

S. Ren, K. He, R. Girshick, and J. Sun, Faster r-cnn: Towards real-time object detection with region proposal networks, NIPS, 2015.

M. Rochan and Y. Wang, Weakly supervised localization of novel objects using appearance transfer, CVPR, pp.4315-4324, 2015.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh et al., Imagenet large scale visual recognition challenge, 2014.

Y. Shen, R. Ji, Y. Wang, Y. Wu, and L. Cao, Cyclic guidance for weakly supervised joint detection and segmentation, CVPR, vol.7, 2006.

M. Shi, H. Caesar, and V. Ferrari, Weakly supervised object localization using things and stuff transfer, ICCV, vol.2, p.3, 2017.

M. Shi and V. Ferrari, Weakly supervised object localization using size estimates, ECCV, 2007.

Z. Shi, P. Siva, and T. Xiang, Transfer learning by ranking for weakly supervised object annotation, 2017.

M. Siam, B. N. Oreshkin, and M. Jagersand, AMP: Adaptive masked proxies for few-shot segmentation, ICCV, October, 2019.

K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, 2014.

K. K. Singh and Y. J. Lee, You reap what you sow: Using videos to generate high precision object proposals for weakly-supervised object detection, CVPR, vol.6, p.7, 2019.

J. Snell, K. Swersky, and R. Zemel, Prototypical networks for few-shot learning, NIPS, vol.3, 2017.

H. O. Song, Y. J. Lee, S. Jegelka, and T. Darrell, Weaklysupervised discovery of visual pattern configurations, NIPS, 2014.

P. Tang, X. Wang, S. Bai, W. Shen, X. Bai et al., Pcl: Proposal cluster learning for weakly supervised object detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.7, issue.8, 2006.

P. Tang, X. Wang, X. Bai, and W. Liu, Multiple instance detection network with online instance classifier refinement, CVPR, vol.7, 2006.

P. Tang, X. Wang, A. Wang, Y. Yan, W. Liu et al., Weakly supervised region proposal network and object detection, ECCV, vol.6, p.7, 2005.

Y. Tang, J. Wang, B. Gao, E. Dellandréa, R. Gaizauskas et al., Large scale semi-supervised object detection using visual and semantic knowledge transfer, CVPR, vol.1, p.3, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01488579

Q. Tao, H. Yang, and J. Cai, Exploiting web images for weakly supervised object detection, IEEE Transactions on Multimedia, vol.6, issue.3, p.7, 2018.

A. Tarvainen and H. Valpola, Mean teachers are better role models: Weight-averaged consistency targets improve semisupervised deep learning results, NIPS, vol.3, p.7, 2017.

J. Uijlings, S. Popov, and V. Ferrari, Revisiting knowledge transfer for training object class detectors, CVPR, pp.1101-1110, 2018.

O. Vinyals, C. Blundell, T. Lillicrap, and D. Wierstra, Matching networks for one shot learning, NIPS, 2016.

Y. Wei, W. Xia, M. Lin, J. Huang, B. Ni et al., Hcp: A flexible cnn framework for multi-label image classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.38, issue.9, pp.1901-1907, 2016.

J. Weston, F. Ratle, and R. Collobert, Deep learning via semi-supervised embedding, ICML, vol.2, p.3, 2008.

Z. Yan, J. Liang, W. Pan, J. Li, and C. Zhang, Weaklyand semi-supervised object detection with expectationmaximization algorithm, vol.1, p.3, 2017.

X. Zhang, J. Feng, H. Xiong, and Q. Tian, Zigzag learning for weakly supervised object detection, CVPR, vol.7, 2005.

X. Zhang, Y. Wei, Y. Yang, and T. Huang, Sg-one: Similarity guidance network for one-shot semantic segmentation, 2018.

F. Zhu, H. Li, W. Ouyang, N. Yu, and X. Wang, Learning spatial regularization with image-level supervisions for multi-label image classification, CVPR, 2017.

C. L. Zitnick and P. Dollár, Edge boxes: Locating object proposals from edges, ECCV, vol.4, p.5, 2014.