Computer Science > Computer Vision and Pattern Recognition
arXiv:2209.12061 (cs)
[Submitted on 24 Sep 2022]
Title:Global Semantic Descriptors for Zero-Shot Action Recognition
View a PDF of the paper titled Global Semantic Descriptors for Zero-Shot Action Recognition, by Valter Estevam and 3 other authors
View PDFAbstract:The success of Zero-shot Action Recognition (ZSAR) methods is intrinsically related to the nature of semantic side information used to transfer knowledge, although this aspect has not been primarily investigated in the literature. This work introduces a new ZSAR method based on the relationships of actions-objects and actions-descriptive sentences. We demonstrate that representing all object classes using descriptive sentences generates an accurate object-action affinity estimation when a paraphrase estimation method is used as an embedder. We also show how to estimate probabilities over the set of action classes based only on a set of sentences without hard human labeling. In our method, the probabilities from these two global classifiers (i.e., which use features computed over the entire video) are combined, producing an efficient transfer knowledge model for action classification. Our results are state-of-the-art in the Kinetics-400 dataset and are competitive on UCF-101 under the ZSAR evaluation. Our code is available atthis https URL
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2209.12061 [cs.CV] |
| (orarXiv:2209.12061v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2209.12061 arXiv-issued DOI via DataCite | |
| Journal reference: | IEEE Signal Processing Letters, vol. 29, pp. 1843-1847, 2022 |
| Related DOI: | https://doi.org/10.1109/LSP.2022.3200605 DOI(s) linking to related resources |
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View a PDF of the paper titled Global Semantic Descriptors for Zero-Shot Action Recognition, by Valter Estevam and 3 other authors
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