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A New Split for Evaluating True Zero-Shot Action Recognition [article]

Shreyank N Gowda, Laura Sevilla-Lara, Kiyoon Kim, Frank Keller, Marcus Rohrbach
2021 arXiv   pre-print
In this paper, we propose a new split for true zero-shot action recognition with no overlap between unseen test classes and training or pre-training classes.  ...  We benchmark several recent approaches on the proposed True Zero-Shot(TruZe) Split for UCF101 and HMDB51, with zero-shot and generalized zero-shot evaluation.  ...  Results with different splits for Zero-Shot Learning (ZSL).  ... 
arXiv:2107.13029v2 fatcat:dirm6mbfajardfxmi3fjgxkhfy

Skeleton based Zero Shot Action Recognition in Joint Pose-Language Semantic Space [article]

Bhavan Jasani, Afshaan Mazagonwalla
2019 arXiv   pre-print
In this work, we present a body pose based zero shot action recognition network and demonstrate its performance on the NTU RGB-D dataset.  ...  Such questions are addressed by the Zero Shot Learning paradigm, where a model is trained on only a subset of classes and is evaluated on its ability to correctly classify an example from a class it has  ...  New datasets for action recognition are frequently being released, and the number of action categories keeps on increasing.  ... 
arXiv:1911.11344v1 fatcat:5adeuam35vd5fclts2rzis3wby

Informed Democracy: Voting-based Novelty Detection for Action Recognition [article]

Alina Roitberg, Ziad Al-Halah, Rainer Stiefelhagen
2018 arXiv   pre-print
Additionally, by combining our model with off-the-shelf zero-shot learning (ZSL) approaches, our model leads to a significant improvement in action classification accuracy for the generalized ZSL setting  ...  While it is common in activity recognition to assume a closed-set setting, i.e. test samples are always of training categories, this assumption is impractical in a real-world scenario.  ...  to a standard classifier or a zero-shot model accordingly. 3) We extend the custom evaluation setup for action recognition to the open-set scenario and formalize the evaluation protocol for the tasks  ... 
arXiv:1810.12819v1 fatcat:a3e5lkh4abdv7nb5qvycfwqq6i

All About Knowledge Graphs for Actions [article]

Pallabi Ghosh, Nirat Saini, Larry S. Davis, Abhinav Shrivastava
2020 arXiv   pre-print
In this paper, we intend to gain a better understanding of knowledge graphs (KGs) that can be utilized for zero-shot and few-shot action recognition.  ...  Finally, to enable a systematic study of zero-shot and few-shot approaches, we propose an improved evaluation paradigm based on UCF101, HMDB51, and Charades datasets for knowledge transfer from models  ...  KG3) from a few examples from the test classes (c). tion for action classes and showing the advantages of a sentence2vector model in capturing the semantics of word sequences for zero/few-shot action recognition  ... 
arXiv:2008.12432v1 fatcat:3mnx3orvhreg3d43a2pmr7b5wq

AI Challenger : A Large-scale Dataset for Going Deeper in Image Understanding [article]

Jiahong Wu, He Zheng, Bo Zhao, Yixin Li, Baoming Yan, Rui Liang, Wenjia Wang, Shipei Zhou, Guosen Lin, Yanwei Fu, Yizhou Wang, Yonggang Wang
2017 arXiv   pre-print
In addition, for related tasks, others can also use our dataset as a new resource to pre-train their models.  ...  The proposed dataset is an effective benchmark to evaluate and improve different computational methods.  ...  We can find that the zero-shot recognition accuracies on the five splits are balanced. MDP achieves the best performance, averagely 48.58%.  ... 
arXiv:1711.06475v1 fatcat:qrwvpy4rwfehvnwfngqvasd72a

Transductive Zero-Shot Action Recognition by Word-Vector Embedding [article]

Xun Xu, Timothy Hospedales, Shaogang Gong
2016 arXiv   pre-print
Instead of collecting ever more data and labelling them exhaustively for all categories, an attractive alternative approach is zero-shot learning" (ZSL).  ...  To that end, in this study we construct a mapping between visual features and a semantic descriptor of each action category, allowing new categories to be recognised in the absence of any visual training  ...  Zero-shot Learning on Actions and Events Data Split: Because there is no existing zero-shot learning evaluation protocol for most existing action and event datasets we propose our own splits 3 .  ... 
arXiv:1511.04458v2 fatcat:yxfn52pdhjfatedmixz4evtiay

Zero-Shot Action Recognition with Transformer-based Video Semantic Embedding [article]

Keval Doshi, Yasin Yilmaz
2022 arXiv   pre-print
In this work, we take a new comprehensive look at the inductive zero-shot action recognition problem from a realistic standpoint.  ...  While video action recognition has been an active area of research for several years, zero-shot action recognition has only recently started gaining traction.  ...  , 44] for zero-shot action recognition.  ... 
arXiv:2203.05156v1 fatcat:ny7p72hia5govbg2g6qboew3da

Transductive Zero-Shot Action Recognition by Word-Vector Embedding

Xun Xu, Timothy Hospedales, Shaogang Gong
2017 International Journal of Computer Vision  
The results demonstrate that our approach achieves the state-of-the-art zero-shot action recognition performance with a simple and efficient pipeline, and without supervised annotation of attributes.  ...  We evaluate extensively our model on a wide range of human action datasets including HMDB51, UCF101, OlympicSports and event datasets including CCV and TRECVID MED 13.  ...  Zero-shot Learning on Actions and Events Data Split: Because there is no existing zero-shot learning evaluation protocol for most existing action and event datasets we propose our own splits 3 .  ... 
doi:10.1007/s11263-016-0983-5 fatcat:c6rn4jpg3ff5pbks52ohlcafny

Elaborative Rehearsal for Zero-shot Action Recognition [article]

Shizhe Chen, Dong Huang
2021 arXiv   pre-print
The growing number of action classes has posed a new challenge for video understanding, making Zero-Shot Action Recognition (ZSAR) a thriving direction.  ...  Moreover, we propose a new ZSAR evaluation protocol on the Kinetics dataset to overcome limitations of current benchmarks and demonstrate the first case where ZSAR performance is comparable to few-shot  ...  Except using different features, the ZSL methods in image domain can be applied for zero-shot action recognition. Zero Shot Action Recognition.  ... 
arXiv:2108.02833v2 fatcat:ttlk5tqtjrf5vb7pmssfbb5cgu

Multi-Label Zero-Shot Human Action Recognition via Joint Latent Ranking Embedding [article]

Qian Wang, Ke Chen
2019 arXiv   pre-print
We evaluate our framework with different settings, including a novel data split scheme designed especially for evaluating multi-label zero-shot learning, on two datasets: Breakfast and Charades.  ...  Consequently, our framework leads to a joint latent ranking embedding for multi-label zero-shot human action recognition.  ...  Acknowledgement The authors would like to thank the anonymous reviewers for their valuable comments that improve the presentation of this manuscript.  ... 
arXiv:1709.05107v3 fatcat:4etrngc7wbhapc4bepxvvolppu

Retro-Actions: Learning 'Close' by Time-Reversing 'Open' Videos

Will Price, Dima Damen
2019 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)  
Finally, we test our approach on two datasets, Jester and Something-Something, evaluating the three video transforms for zero-shot learning and data augmentation.  ...  Label transforms offer additional supervision previously unexplored in video recognition benefiting data augmentation and enabling zero-shot learning opportunities by learning a class from transformed  ...  Top Row: A zero-shot model trained only on left-to-right examples can correctly classify zero-shot right-to-left actions.  ... 
doi:10.1109/iccvw.2019.00173 dblp:conf/iccvw/PriceD19 fatcat:kozq5nycrnfl7ioirim3mjgfyu

Retro-Actions: Learning 'Close' by Time-Reversing 'Open' Videos [article]

Will Price, Dima Damen
2019 arXiv   pre-print
Finally, we test our approach on two datasets, Jester and Something-Something, evaluating the three video transforms for zero-shot learning and data augmentation.  ...  Label transforms offer additional supervision previously unexplored in video recognition benefiting data augmentation and enabling zero-shot learning opportunities by learning a class from transformed  ...  Top Row: A zero-shot model trained only on left-to-right examples can correctly classify zero-shot right-to-left actions.  ... 
arXiv:1909.09422v1 fatcat:ifesd4o6erfyndq3b6eikfvdpm

CLASTER: Clustering with Reinforcement Learning for Zero-Shot Action Recognition [article]

Shreyank N Gowda, Laura Sevilla-Lara, Frank Keller, Marcus Rohrbach
2021 arXiv   pre-print
Zero-shot action recognition is the task of recognizingaction classes without visual examples, only with a seman-tic embedding which relates unseen to seen classes.  ...  evaluation and the generalized zero-shotlearning.  ...  We call our proposed method CLASTER, for CLustering for Action recognition in zero-ShoT lEaRning, and show that it significantly outperforms all existing methods across all standard zero-shot action recognition  ... 
arXiv:2101.07042v2 fatcat:w5qvvnv3rjdotaqadf5k4v6fvq

Tell me what you see: A zero-shot action recognition method based on natural language descriptions [article]

Valter Estevam and Rayson Laroca and David Menotti and Helio Pedrini
2021 arXiv   pre-print
Recently, several approaches have explored the detection and classification of objects in videos to perform Zero-Shot Action Recognition with remarkable results.  ...  We show that this pre-training is essential for bridging the semantic gap.  ...  A new split for evaluating true zero-shot action tion Processing Systems (NeurIPS), pp. 3111–3119.  ... 
arXiv:2112.09976v1 fatcat:5bvci2dyjnbyjbvo7qagnuzbpy

Zero-Shot Action Recognition in Videos: A Survey [article]

Valter Estevam, Helio Pedrini, David Menotti
2020 arXiv   pre-print
specifically zero-shot action recognition in videos.  ...  Zero-Shot Action Recognition has attracted attention in the last years and many approaches have been proposed for recognition of objects, events and actions in images and videos.  ...  Method Used in appoaches A Annotated Wang and Chen [90] Mishra et al. [  ... 
arXiv:1909.06423v2 fatcat:w5eh7wjdmnaktnbsqczsdmhane
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