Fetching the paper…
Reading the bibliography…
Multi-label zero-shot learning strives to classify images into multiple unseen categories for which no data is available during training.
G. Tsoumakas and I. Katakis, “Multi-label classification: An overview,”
2007
Earlier work this paper cites.
M.-E. Nilsback and A. Zisserman, “Automated flower classification over a large number of classes,” in
2008
Earlier work this paper cites.
T.-S. Chua, J. Tang, R. Hong, H. Li, Z. Luo, and Y. Zheng, “Nus-wide: a real-world web image database from national university of singapore,” in
2009
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in
2009
Earlier work this paper cites.
P. Welinder, S. Branson, T. Mita, C. Wah, F. Schroff, S. Belongie, and P. Perona, “Caltech-ucsd birds 200,”
2010
Earlier work this paper cites.
J. Weston, S. Bengio, and N. Usunier, “Wsabie: Scaling up to large vocabulary image annotation,” in
2011
Earlier work this paper cites.
A. Frome, G. S. Corrado, J. Shlens, S. Bengio, J. Dean, M. Ranzato, and T. Mikolov, “Devise: A deep visual-semantic embedding model,” in
2013
Earlier work this paper cites.
M. Rohrbach, S. Ebert, and B. Schiele, “Transfer learning in a transductive setting,” in
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in
2014
Earlier work this paper cites.
T. Mensink, E. Gavves, and C. G. Snoek, “Costa: Co-occurrence statistics for zero-shot classification,” in
2014
Earlier work this paper cites.
D. Jayaraman and K. Grauman, “Zero-shot recognition with unreliable attributes,” in
2014
Earlier work this paper cites.
I. Goodfellow, J. PougetAbadie, M. Mirza, B. Xu, and D. Warde-Farley, “Generative adversarial nets,” in
2014
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,”
2014
Earlier work this paper cites.
J. Pennington, R. Socher, and C. D. Manning, “Glove: Global vectors for word representation,” in
2014
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,”
2014
Earlier work this paper cites.
Y. Fu, T. M. Hospedales, T. Xiang, and S. Gong, “Transductive multi-view zero-shot learning,”
2015
Earlier work this paper cites.
B. Romera-Paredes and P. Torr, “An embarrassingly simple approach to zero-shot learning,” in
2015
Earlier work this paper cites.
Z. Akata, F. Perronnin, Z. Harchaoui, and C. Schmid, “Label-embedding for image classification,”
2015
Cited alongside, same era.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in
2015
Cited alongside, same era.
J. Wang, Y. Yang, J. Mao, Z. Huang, C. Huang, and W. Xu, “Cnn-rnn: A unified framework for multi-label image classification,” in
2016
Cited alongside, same era.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,”
2016
Cited alongside, same era.
Y. Zhang, B. Gong, and M. Shah, “Fast zero-shot image tagging,” in
2016
Cited alongside, same era.
J.-H. Kim, J. Jun, and B.-T. Zhang, “Bilinear attention networks,” in
2018
Later among the works it cites.
S. Rahman, S. Khan, and N. Barnes, “Polarity loss for zero-shot object detection,”
2018
Later among the works it cites.
Z.-M. Chen, X.-S. Wei, P. Wang, and Y. Guo, “Multi-label image recognition with graph convolutional networks,” in
2019
Later among the works it cites.
T. Durand, N. Mehrasa, and G. Mori, “Learning a deep convnet for multi-label classification with partial labels,” in
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
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Z. Wang, T. Chen, G. Li, R. Xu, and L. Lin, “Multi-label image recognition by recurrently discovering attentional regions,” in
2017
Cited alongside, same era.
J. Nam, E. Loza Mencía, H. J. Kim, and J. Fürnkranz, “Maximizing subset accuracy with recurrent neural networks in multi-label classification,”
2017
Cited alongside, same era.
M. Ye and Y. Guo, “Zero-shot classification with discriminative semantic representation learning,” in
2017
Cited alongside, same era.
M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein gan,”
2017
Cited alongside, same era.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in
2017
Cited alongside, same era.
A. Veit, N. Alldrin, G. Chechik, I. Krasin, A. Gupta, and S. Belongie, “Learning from noisy large-scale datasets with minimal supervision,” in
2017
Cited alongside, same era.
B. Zhou, A. Lapedriza, A. Khosla, A. Oliva, and A. Torralba, “Places: A 10 million image database for scene recognition,”
2017
Cited alongside, same era.
J. Li, M. Jing, K. Lu, Z. Ding, L. Zhu, and Z. Huang, “Leveraging the invariant side of generative zero-shot learning,” in
2019
Later among the works it cites.
H. Huang, C. Wang, P. S. Yu, and C.-D. Wang, “Generative dual adversarial network for generalized zero-shot learning,” in
2019
Later among the works it cites.
D. Mandal, S. Narayan, S. K. Dwivedi, V. Gupta, S. Ahmed, F. S. Khan, and L. Shao, “Out-of-distribution detection for generalized zero-shot action recognition,” in
2019
Later among the works it cites.
J. Ye, J. He, X. Peng, W. Wu, and Y. Qiao, “Attention-driven dynamic graph convolutional network for multi-label image recognition,” in
2020
Later among the works it cites.
R. You, Z. Guo, L. Cui, X. Long, Y. Bao, and S. Wen, “Cross-modality attention with semantic graph embedding for multi-label classification.” in
2020
Later among the works it cites.
V. O. Yazici, A. Gonzalez-Garcia, A. Ramisa, B. Twardowski, and J. v. d. Weijer, “Orderless recurrent models for multi-label classification,” in
2020
Later among the works it cites.
D. Huynh and E. Elhamifar, “A shared multi-attention framework for multi-label zero-shot learning,” in
2020
Later among the works it cites.
2020
Later among the works it cites.
N. Hayat, M. Hayat, S. Rahman, S. Khan, S. W. Zamir, and F. S. Khan, “Synthesizing the unseen for zero-shot object detection,” in
2020
Later among the works it cites.
G. Ou, G. Yu, C. Domeniconi, X. Lu, and X. Zhang, “Multi-label zero-shot learning with graph convolutional networks,”
2020
Later among the works it cites.
Z. Ji, B. Cui, H. Li, Y.-G. Jiang, T. Xiang, T. Hospedales, and Y. Fu, “Deep ranking for image zero-shot multi-label classification,”
2020
Later among the works it cites.
M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski, and A. Joulin, “Emerging properties in self-supervised vision transformers,” in
2021
Closest in time.
S. Narayan, A. Gupta, S. Khan, F. S. Khan, L. Shao, and M. Shah, “Discriminative region-based multi-label zero-shot learning,” in
2021
Closest in time.