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Zero-shot learning has been actively studied for image classification task to relieve the burden of annotating image labels.
C. H. Lampert, H. Nickisch, and S. Harmeling, “Learning to detect unseen object classes by between-class attribute transfer,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , Miami, FL, Jun. 2009
2009
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2011
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C. H. Lampert, H. Nickisch, and S. Harmeling, “Attribute-based classification for zero-shot visual object categorization,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 36, no. 3, pp. 453–465, 2013
2013
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T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” in Advances in Neural Information Processing Systems , Lake Tahoe, Nevada, United States, Dec. 2013
2013
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A. Frome, G. S. Corrado, J. Shlens, S. Bengio, J. Dean, M. Ranzato, and T. Mikolov, “Devise: A deep visual-semantic embedding model,” in Advances in Neural Information Processing Systems , Lake Tahoe, Nevada, United States, Dec. 2013
2013
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2013
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A. Habibian, T. Mensink, and C. G. M. Snoek, “Composite concept discovery for zero-shot video event detection,” in ACM Multimedia , Orlando, FL, Oct. 2014
2014
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R. Mottaghi, X. Chen, X. Liu, N.-G. Cho, S.-W. Lee, S. Fidler, R. Urtasun, and A. Yuille, “The role of context for object detection and semantic segmentation in the wild,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , Columbus, OH, Jun. 2014
2014
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I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems , Montreal, Canada, Dec. 2014
2014
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J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , Boston, MA, Jun. 2015
2015
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O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-assisted Intervention , Munich, Germany, Oct. 2015, pp. 234–241
2015
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G. Papandreou, L.-C. Chen, K. P. Murphy, and A. L. Yuille, “Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation,” in Proceedings of the IEEE International Conference on Computer Vision , Santiago, Chile, Dec. 2015
2015
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X. Yao, J. Han, C. Gong, and G. Lei, “Semantic segmentation based on stacked discriminative autoencoders and context-constrained weakly supervised learning,” in ACM Multimedia , Sydney, Australia, Oct. 2015
2015
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Z. Akata, F. Perronnin, Z. Harchaoui, and C. Schmid, “Label-embedding for image classification,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 38, no. 7, pp. 1425–1438, 2015
2015
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B. Romera-Paredes and P. Torr, “An embarrassingly simple approach to zero-shot learning,” in Proceedings of the IEEE International Conference on Machine Learning , Lille, France, Jul. 2015
2015
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Y. Fu, T. M. Hospedales, T. Xiang, and S. Gong, “Transductive multi-view zero-shot learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 37, no. 11, pp. 2332–2345, 2015
2015
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O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “Imagenet large scale visual recognition challenge,” IJCV , vol. 115, no. 3, pp. 211–252, 2015
2015
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M. Everingham, S. M. A. Eslami, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes challenge: A retrospective,” International Journal of Computer Vision , vol. 111, no. 1, pp. 98–136, 2015
2015
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Y. Yang, Y. Luo, W. Chen, F. Shen, J. Shao, and H. T. Shen, “Zero-shot hashing via transferring supervised knowledge,” in ACM Multimedia , Amsterdam, Netherlands, Oct. 2016
2016
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M. Mathieu, C. Couprie, and Y. LeCun, “Deep multi-scale video prediction beyond mean square error,” in International Conference on Learning Representations , San Juan, Puerto Rico, May 2016
2016
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D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros, “Context encoders: Feature learning by inpainting,” in Proceedings of the IEEE conference on computer vision and pattern recognition , Las Vegas, NV, Jun. 2016, pp. 2536–2544
2016
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A. v. d. Oord, N. Kalchbrenner, and K. Kavukcuoglu, “Pixel recurrent neural networks,” in Proceedings of the IEEE International Conference on Machine Learning , New York, USA, Jun. 2016
2016
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D. Lin, J. Dai, J. Jia, K. He, and J. Sun, “Scribblesup: Scribble-supervised convolutional networks for semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , Las Vegas, NV, Jul. 2016
2016
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F. Yu and V. Koltun, “Multi-scale context aggregation by dilated convolutions,” in International Conference on Learning Representations , San Juan, Puerto Rico, May 2016
2016
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K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , Las Vegas, NV, Jun. 2016
2016
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W. Luo, Y. Li, R. Urtasun, and R. S. Zemel, “Understanding the effective receptive field in deep convolutional neural networks,” in Advances in Neural Information Processing Systems , Barcelona, Spain, Dec. 2016
2016
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Y. Guo, G. Ding, J. Han, and Y. Gao, “Zero-shot learning with transferred samples,” IEEE Transactions on Image Processing , vol. 26, no. 7, pp. 3277–3290, 2017
2017
Cited alongside, same era.
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, “Pyramid scene parsing network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , Honolulu, HI, Jul. 2017
2017
Cited alongside, same era.
G. Lin, A. Milan, C. Shen, and I. Reid, “Refinenet: Multi-path refinement networks for high-resolution semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , Honolulu, HI, Jul. 2017
2017
Cited alongside, same era.
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , Honolulu, HI, Jul. 2017
2017
Cited alongside, same era.
D. Xu, W. Ouyang, X. Wang, and N. Sebe, “Pad-net: Multi-tasks guided prediction-and-distillation network for simultaneous depth estimation and scene parsing,” in Proceedings of the European Conference on Computer Vision , Munich,Germany, Sep. 2018, pp. 675–684
2018
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J. Hu, L. Shen, S. Albanie, G. Sun, and A. Vedaldi, “Gather-excite: Exploiting feature context in convolutional neural networks,” in Advances in Neural Information Processing Systems , Montral,Canada, Dec. 2018
2018
Later among the works it cites.
H. Caesar, J. Uijlings, and V. Ferrari, “Coco-stuff: Thing and stuff classes in context,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , Salt Lake City, UT, Jun. 2018
2018
Later among the works it cites.
J. Li, M. Jin, K. Lu, Z. Ding, L. Zhu, and Z. Huang, “Leveraging the invariant side of generative zero-shot learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , Long Beach, CA, Jun. 2019
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J.-Y. Zhu, R. Zhang, D. Pathak, T. Darrell, A. A. Efros, O. Wang, and E. Shechtman, “Toward multimodal image-to-image translation,” in Advances in Neural Information Processing Systems , Long Beach, CA, Dec. 2017
2017
Cited alongside, same era.
S. J. Oh, R. Benenson, A. Khoreva, Z. Akata, M. Fritz, and B. Schiele, “Exploiting saliency for object segmentation from image level labels,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , Honolulu, HI, Jul. 2017
2017
Cited alongside, same era.
A. Khoreva, R. Benenson, J. Hosang, M. Hein, and B. Schiele, “Simple does it: Weakly supervised instance and semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , Honolulu, HI, Jul. 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
S. Caelles, K.-K. Maninis, J. Pont-Tuset, L. Leal-Taixé, D. Cremers, and L. Van Gool, “One-shot video object segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , Hawaii,USA, Jul. 2017, pp. 221–230
2017
Cited alongside, same era.
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang et al. , “Photo-realistic single image super-resolution using a generative adversarial network,” in Proceedings of the IEEE conference on computer vision and pattern recognition , Hawaii,USA, Jul. 2017, pp. 4681–4690
2017
Cited alongside, same era.
X. Mao, Q. Li, H. Xie, R. Y. K. Lau, Z. Wang, and S. P. Smolley, “Least squares generative adversarial networks,” in Proceedings of the IEEE International Conference on Computer Vision , Venice, Italy, Oct. 2017
2017
Cited alongside, same era.
H. Zhang, T. Xu, H. Li, S. Zhang, X. Wang, X. Huang, and D. N. Metaxas, “Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks,” in Proceedings of the IEEE international conference on computer vision , Venice, Italy, Oct. 2017, pp. 5907–5915
2017
Cited alongside, same era.
2019
Later among the works it cites.
D. Mandal, S. Narayan, S. Dwivedi, V. Gupta, S. Ahmed, F. S. Khan, and L. Shao, “Out-of-distribution detection for generalized zero-shot action recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , Long Beach, CA, Jun. 2019
2019
Later among the works it cites.
Z. Zhang, A. Chen, L. Xie, J. Yu, and S. Gao, “Learning semantics-aware distance map with semantics layering network for amodal instance segmentation,” in ACM Multimedia , Nice, France, Oct. 2019
2019
Later among the works it cites.
J. Fu, J. Liu, Y. Wang, J. Zhou, C. Wang, and H. Lu, “Stacked deconvolutional network for semantic segmentation,” IEEE Transactions on Image Processing , pp. 1–1, 2019
2019
Later among the works it cites.
M. Bucher, T.-H. Vu, M. Cord, and P. Pérez, “Zero-shot semantic segmentation,” in Advances in Neural Information Processing Systems , Vancouver, Canada, May 2019
2019
Later among the works it cites.
N. Kato, T. Yamasaki, and K. Aizawa, “Zero-shot semantic segmentation via variational mapping,” in Proceedings of the IEEE International Conference on Computer Vision Workshops , Seoul, Korea, Oct. 2019
2019
Later among the works it cites.
Y. Xian, S. Choudhury, Y. He, B. Schiele, and Z. Akata, “Semantic projection network for zero-and few-label semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , Long Beach, CA, Jun. 2019
2019
Later among the works it cites.
C. Zhang, G. Lin, F. Liu, J. Guo, Q. Wu, and R. Yao, “Pyramid graph networks with connection attentions for region-based one-shot semantic segmentation,” in Proceedings of the IEEE International Conference on Computer Vision Workshops , Seoul, Korea, Oct. 2019
2019
Later among the works it cites.
Y. Xian, S. Sharma, B. Schiele, and Z. Akata, “f-vaegan-d2: A feature generating framework for any-shot learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , Long Beach, CA, Jun. 2019
2019
Later among the works it cites.
M. B. Sariyildiz and R. G. Cinbis, “Gradient matching generative networks for zero-shot learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , Long Beach, CA, Jun. 2019
2019
Later among the works it cites.
J. Zhang, L. Niu, D. Yang, L. Kang, Y. Li, W. Zhao, and L. Zhang, “Gain: Gradient augmented inpainting network for irregular holes,” in Proceedings of the 27th ACM International Conference on Multimedia , Nice, France, Oct. 2019, pp. 1870–1878
2019
Later among the works it cites.
D. Huynh and E. Elhamifar, “Fine-grained generalized zero-shot learning via dense attribute-based attention,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , Jun. 2020
2020
Closest in time.
Y. Yu, Z. Ji, J. Han, and Z. Zhang, “Episode-based prototype generating network for zero-shot learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , Jun. 2020
2020
Closest in time.
H. Bi, L. Xu, X. Cao, Y. Xue, and Z. Xu, “Polarimetric sar image semantic segmentation with 3d discrete wavelet transform and markov random field,” IEEE Transactions on Image Processing , vol. 29, pp. 6601–6614, 2020
2020
Closest in time.
J. Fu, J. Liu, J. Jiang, Y. Li, Y. Bao, and H. Lu, “Scene segmentation with dual relation-aware attention network,” IEEE Transactions on Neural Networks and Learning Systems , pp. 1–14, 2020
2020
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2020
Closest in time.
F. Lv, H. Liu, Y. Wang, J. Zhao, and G. Yang, “Learning unbiased zero-shot semantic segmentation networks via transductive transfer,” IEEE Signal Processing Letters , vol. 27, pp. 1640–1644, 2020
2020
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P. Li, Y. Wei, and Y. Yang, “Consistent structural relation learning for zero-shot segmentation,” in Advances in Neural Information Processing Systems , Virtual-only, Dec. 2020
2020
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P. Hu, S. Sclaroff, and K. Saenko, “Uncertainty-aware learning for zero-shot semantic segmentation,” in Advances in Neural Information Processing Systems , Virtual-only, Dec. 2020
2020
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Z. Gu, S. Zhou, L. Niu, Z. Zhao, and L. Zhang, “Context-aware feature generation for zero-shot semantic segmentation,” in ACM Multimedia , Seattle, United States, Oct. 2020
2020
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M. Liu, L. Qu, L. Nie, M. Liu, L. Duan, and B. Chen, “Iterative local-global collaboration learning towards one-shot video person re-identification,” IEEE Transactions on Image Processing , vol. 29, pp. 9360–9372, 2020
2020
Closest in time.
X. Zhang, Y. Wei, Y. Yang, and T. S. Huang, “Sg-one: Similarity guidance network for one-shot semantic segmentation,” IEEE Transactions on Cybernetics , vol. 50, no. 9, pp. 3855–3865, 2020
2020
Closest in time.