Mandal, D and Narayan, S and Dwivedi, SK and Gupta, V and Ahmed, S and Khan, FS and Shao, L (2019) Out-of-distribution detection for generalized zero-shot action recognition. In: 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019, 16 - 20 June 2019, Long Beach, pp. 9977-9985.
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Abstract
Generalized zero-shot action recognition is a challenging problem, where the task is to recognize new action categories that are unavailable during the training stage, in addition to the seen action categories. Existing approaches suffer from the inherent bias of the learned classifier towards the seen action categories. As a consequence, unseen category samples are incorrectly classified as belonging to one of the seen action categories. In this paper, we set out to tackle this issue by arguing for a separate treatment of seen and unseen action categories in generalized zero-shot action recognition. We introduce an out-of-distribution detector that determines whether the video features belong to a seen or unseen action category. To train our out-of-distribution detector, video features for unseen action categories are synthesized using generative adversarial networks trained on seen action category features. To the best of our knowledge, we are the first to propose an out-of-distribution detector based GZSL framework for action recognition in videos. Experiments are performed on three action recognition datasets: Olympic Sports, HMDB51 and UCF101. For generalized zero-shot action recognition, our proposed approach outperforms the baseline with absolute gains (in classification accuracy) of 7.0, 3.4, and 4.9, respectively, on these datasets.
| Item Type: | Conference Paper |
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| Publication: | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
| Publisher: | IEEE Computer Society |
| Additional Information: | The copyright for this article belongs to the Authors. |
| Keywords: | Classification (of information); Computer vision, Absolute gain; Action recognition; Adversarial networks; Categorization; Classification accuracy; Olympics; Retrieval; Video features, Feature extraction |
| Department/Centre: | Division of Electrical Sciences > Electrical Engineering |
| Date Deposited: | 25 Oct 2022 08:41 |
| Last Modified: | 25 Oct 2022 08:41 |
| URI: | https://eprints.iisc.ac.in/id/eprint/77523 |
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