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Current action recognition systems require large amounts of training data for recognizing an action.
Action2vec: A crossmodal embedding approach to action learning
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Ji, S., Xu, W., Yang, M., and Yu, K. (2013) · 2013
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Linguistic regularities in continuous space word representations
Mikolov, T., Yih, W.-t., and Zweig, G. (2013c) · 2013
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Attribute-based classification for zero-shot visual object categorization
Lampert, C. H., Nickisch, H., and Harmeling, S. (2014) · 2014
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Two-stream convolutional networks for action recognition in videos
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What do 15, 000 object categories tell us about classifying and localizing actions?
Jain, M., van Gemert, J. C., and Snoek, C. G. M. (2015b) · 2015
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Kodirov, E., Xiang, T., Fu, Z., and Gong, S. (2015) · 2015
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Learning entity and relation embeddings for knowledge graph completion
Lin, Y., Liu, Z., Sun, M., Liu, Y., and Zhu, X. (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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Learning spatiotemporal features with 3d convolutional networks
Tran, D., Bourdev, L., Fergus, R., Torresani, L., and Paluri, M. (2015) · 2015
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Semantic embedding space for zero-shot action recognition
Xu, X., Hospedales, T., and Gong, S. (2015) · 2015
Optimization as a model for few-shot learning
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Conceptnet 5.5: An open multilingual graph of general knowledge
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Transductive zero-shot action recognition by word-vector embedding
Xu, X., Hospedales, T., and Gong, S. (2017) · 2017
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Ava: A video dataset of spatio-temporally localized atomic visual actions
Gu, C., Sun, C., Ross, D. A., Vondrick, C., Pantofaru, C., Li, Y., Vijayanarasimhan, S., Toderici, G., Ricco, S., Sukthankar, R., et al. (2018) · 2018
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Compositional learning for human object interaction
Kato, K., Li, Y., and Gupta, A. (2018) · 2018
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Describing videos by exploiting temporal structure
Yao, L., Torabi, A., Cho, K., Ballas, N., Pal, C., Larochelle, H., and Courville, A. (2015) · 2015
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Exploring synonyms as context in zero-shot action recognition
Alexiou, I., Xiang, T., and Gong, S. (2016) · 2016
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Synthesized classifiers for zero-shot learning
Changpinyo, S., Chao, W.-L., Gong, B., and Sha, F. (2016) · 2016
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Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M., Bresson, X., and Vandergheynst, P. (2016) · 2016
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Recognizing an action using its name: A knowledge-based approach
Gan, C., Yang, Y., Zhu, L., Zhao, D., and Zhuang, Y. (2016) · 2016
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The more you know: Using knowledge graphs for image classification
Marino, K., Salakhutdinov, R., and Gupta, A. (2016) · 2016
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A generative approach to zero-shot and few-shot action recognition
Mishra, A., Verma, V. K., Reddy, M. S. K., Arulkumar, S., Rai, P., and Mittal, A. (2018a) · 2018
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Unsupervised learning of sentence embeddings using compositional n-gram features
Pagliardini, M., Gupta, P., and Jaggi, M. (2018) · 2018
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Meta-learning for semi-supervised few-shot classification
Ren, M., Triantafillou, E., Ravi, S., Snell, J., Swersky, K., Tenenbaum, J. B., Larochelle, H., and Zemel, R. S. (2018) · 2018
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Learning to compare: Relation network for few-shot learning
Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P. H., and Hospedales, T. M. (2018b) · 2018
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A closer look at spatiotemporal convolutions for action recognition
Tran, D., Wang, H., Torresani, L., Ray, J., LeCun, Y., and Paluri, M. (2018) · 2018
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Graph attention networks
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y. (2018) · 2018
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Zero-shot recognition via semantic embeddings and knowledge graphs
Wang, X., Ye, Y., and Gupta, A. (2018) · 2018
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S3d: Stacking segmental p3d for action quality assessment
Xiang, X., Tian, Y., Reiter, A., Hager, G. D., and Tran, T. D. (2018) · 2018
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Spatial temporal graph convolutional networks for skeleton-based action recognition
Yan, S., Xiong, Y., and Lin, D. (2018) · 2018
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One-shot action localization by learning sequence matching network
Yang, H., He, X., and Porikli, F. (2018) · 2018
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Compound memory networks for few-shot video classification
Zhu, L. and Yang, Y. (2018) · 2018
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Towards universal representation for unseen action recognition
Zhu, Y., Long, Y., Guan, Y., Newsam, S., and Shao, L. (2018) · 2018
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I know the relationships: Zero-shot action recognition via two-stream graph convolutional networks and knowledge graphs
Gao, J., Zhang, T., and Xu, C. (2019) · 2019
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Protogan: Towards few shot learning for action recognition
Kumar Dwivedi, S., Gupta, V., Mitra, R., Ahmed, S., and Jain, A. (2019) · 2019
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Out-of-distribution detection for generalized zero-shot action recognition
Mandal, D., Narayan, S., Dwivedi, S. K., Gupta, V., Ahmed, S., Khan, F. S., and Shao, L. (2019) · 2019
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Long-tail relation extraction via knowledge graph embeddings and graph convolution networks
Zhang, N., Deng, S., Sun, Z., Wang, G., Chen, X., Zhang, W., and Chen, H. (2019) · 2019
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Stacked spatio-temporal graph convolutional networks for action segmentation
Ghosh, P., Yao, Y., Davis, L., and Divakaran, A. (2020) · 2020
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Gfcn: A new graph convolutional network based on parallel flows
Ji, F., Yang, J., Zhang, Q., and Tay, W. P. (2020) · 2020
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Learning a deep embedding model for zero-shot learning
Zhang, L., Xiang, T., and Gong, S. (2017b) · 2030
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