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Zero-shot learning for visual recognition, e.g., object and action recognition, has recently attracted a lot of attention.
A nonlinear mapping for data structure analysis
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Support vector regression machines
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Multidimensional scaling
Cox, T. F., and Cox, M. A. (2000) · 2000
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An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Cristianini, N., and Shawe-Taylor, J. (2000) · 2000
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Principal Component Analysis
Jolliffe, I. (2002) · 2002
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Canonical correlation analysis: An overview with application to learning methods
Hardoon, D. R., Szedmak, S., and Shawe-Taylor, J. (2004) · 2004
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Locality preserving projections
Niyogi, X. (2004) · 2004
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Supervised kernel locality preserving projections for face recognition
Cheng, J., Liu, Q., Lu, H., and Chen, Y.-W. (2005) · 2005
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Large margin methods for structured and interdependent output variables
Tsochantaridis, I., Joachims, T., Hofmann, T., and Altun, Y. (2005) · 2005
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Semi-supervised discriminant analysis
Cai, D., He, X., and Han, J. (2007) · 2007
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Caltech-256 object category dataset,
Griffin, G., Holub, A., and Perona, P. (2007) · 2007
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Gabor feature-based face recognition using supervised locality preserving projection
Zheng, Z., Yang, F., Tan, W., Jia, J., and Yang, J. (2007) · 2007
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Learning to detect unseen object classes by between-class attribute transfer
Lampert, C. H., Nickisch, H., and Harmeling, S. (2009) · 2009
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Manifold and subspace learning for pattern recognition
Fu, Y., and Huang, T. (2010) · 2010
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Hubs in space: Popular nearest neighbors in high-dimensional data
Radovanović, M., Nanopoulos, A., and Ivanović, M. (2010) · 2010
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Locality preserving and global discriminant projection with prior information
Zhang, H., Deng, W., Guo, J., and Yang, J. (2010) · 2010
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Hmdb: a large video database for human motion recognition
Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., and Serre, T. (2011) · 2011
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Recognizing human actions by attributes
Liu, J., Kuipers, B., and Savarese, S. (2011) · 2011
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The caltech-ucsd birds-200-2011 dataset,
Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S. (2011) · 2011
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UCF101: A dataset of 101 human actions classes from videos in the wild
Soomro, K., Zamir, A. R., and Shah, M. (2012) · 2012
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Label-embedding for attribute-based classification
Akata, Z., Perronnin, F., Harchaoui, Z., and Schmid, C. (2013) · 2013
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50 years of object recognition: Directions forward
Andreopoulos, A., and Tsotsos, J. K. (2013) · 2013
Cited alongside, same era.
Devise: A deep visual-semantic embedding model
Frome, A., Corrado, G. S., Shlens, J., Bengio, S., Dean, J., Mikolov, T. et al. (2013) · 2013
Cited alongside, same era.
Distributed representations of words and phrases and their compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J. (2013) · 2013
Cited alongside, same era.
Classifying web videos using a global video descriptor
Solmaz, B., Assari, S. M., and Shah, M. (2013) · 2013
Cited alongside, same era.
Action recognition with improved trajectories
Wang, H., and Schmid, C. (2013) · 2013
Cited alongside, same era.
Zero-shot learning with structured embeddings
Akata, Z., Lee, H., and Schiele, B. (2014) · 2014
Deep visual-semantic alignments for generating image descriptions
Karpathy, A., and Fei-Fei, L. (2015) · 2015
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Unsupervised domain adaptation for zero-shot learning
Kodirov, E., Xiang, T., Fu, Z., and Gong, S. (2015) · 2015
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An embarrassingly simple approach to zero-shot learning
Romera-Paredes, B., and Torr, P. (2015) · 2015
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Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M. et al. (2015) · 2015
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Ridge regression, hubness, and zero-shot learning
Shigeto, Y., Suzuki, I., Hara, K., Shimbo, M., and Matsumoto, Y. (2015) · 2015
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., and Zisserman, A. (2015) · 2015
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Cited alongside, same era.
Return of the devil in the details: Delving deep into convolutional nets
Chatfield, K., Simonyan, K., Vedaldi, A., and Zisserman, A. (2014) · 2014
Cited alongside, same era.
A multi-view embedding space for modeling internet images, tags, and their semantics
Gong, Y., Ke, Q., Isard, M., and Lazebnik, S. (2014) · 2014
Cited alongside, same era.
Zero-shot recognition with unreliable attributes
Jayaraman, D., and Grauman, K. (2014) · 2014
Cited alongside, same era.
THUMOS challenge: Action recognition with a large number of classes
Jiang, Y.-G., Liu, J., Roshan Zamir, A., Toderici, G., Laptev, I., Shah, M., and Sukthankar, R. (2014) · 2014
Cited alongside, same era.
Attribute-based classification for zero-shot visual object categorization
Lampert, C. H., Nickisch, H., and Harmeling, S. (2014) · 2014
Cited alongside, same era.
COSTA: Co-occurrence statistics for zero-shot classification
Mensink, T., Gavves, E., and Snoek, C. (2014) · 2014
Cited alongside, same era.
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Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015) · 2015
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Learning spatiotemporal features with 3d convolutional networks
Tran, D., Bourdev, L., Fergus, R., Torresani, L., and Paluri, M. (2015) · 2015
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Matconvnet – convolutional neural networks for matlab
Vedaldi, A., and Lenc, K. (2015) · 2015
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Kernelized multiview projection
Yu, M., Liu, L., and Shao, L. (2015) · 2015
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Zero-shot learning via semantic similarity embedding
Zhang, Z., and Saligrama, V. (2015) · 2015
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Pooling the convolutional layers in deep convnets for action recognition
Zhao, S., Liu, Y., Han, Y., and Hong, R. (2015) · 2015
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Label-embedding for image classification
Akata, Z., Perronnin, F., Harchaoui, Z., and Schmid, C. (2016) · 2016
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Learning attributes equals multi-source domain generalization
Gan, C., Yang, T., and Gong, B. (2016) · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
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Bag of visual words and fusion methods for action recognition: Comprehensive study and good practice
Peng, X., Wang, L., Wang, X., and Qiao, Y. (2016) · 2016
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Learning deep representations of fine-grained visual descriptions
Reed, S., Akata, Z., Schiele, B., and Lee, H. (2016) · 2016
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Kernelized multiview projection for robust action recognition
Shao, L., Liu, L., and Yu, M. (2016) · 2016
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Temporal segment networks: Towards good practices for deep action recognition
Wang, L., Xiong, Y., Wang, Z., Qiao, Y., and Van Gool, L. (2016) · 2016
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Multi-stream multi-class fusion of deep networks for video classification
Wu, Z., Jiang, Y.-G., Wang, X., Ye, H., Xue, X., and Wang, J. (2016) · 2016
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Latent embeddings for zero-shot classification
Xian, Y., Akata, Z., Sharma, G., Nguyen, Q., Hein, M., and Schiele, B. (2016) · 2016
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