FineAction: A Fined Video Dataset for Temporal Action Localization
- URL: http://arxiv.org/abs/2105.11107v1
- Date: Mon, 24 May 2021 06:06:32 GMT
- Title: FineAction: A Fined Video Dataset for Temporal Action Localization
- Authors: Yi Liu, Limin Wang, Xiao Ma, Yali Wang, Yu Qiao
- Abstract summary: FineAction is a new large-scale fined video dataset collected from existing video datasets and web videos.
This dataset contains 139K fined action instances densely annotated in almost 17K untrimmed videos spanning 106 action categories.
Experimental results reveal that our FineAction brings new challenges for action localization on fined and multi-label instances with shorter duration.
- Score: 60.90129329728657
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: On the existing benchmark datasets, THUMOS14 and ActivityNet, temporal action
localization techniques have achieved great success. However, there are still
existing some problems, such as the source of the action is too single, there
are only sports categories in THUMOS14, coarse instances with uncertain
boundaries in ActivityNet and HACS Segments interfering with proposal
generation and behavior prediction. To take temporal action localization to a
new level, we develop FineAction, a new large-scale fined video dataset
collected from existing video datasets and web videos. Overall, this dataset
contains 139K fined action instances densely annotated in almost 17K untrimmed
videos spanning 106 action categories. FineAction has a more fined definition
of action categories and high-quality annotations to reduce the boundary
uncertainty compared to the existing action localization datasets. We
systematically investigate representative methods of temporal action
localization on our dataset and obtain some interesting findings with further
analysis. Experimental results reveal that our FineAction brings new challenges
for action localization on fined and multi-label instances with shorter
duration. This dataset will be public in the future and we hope our FineAction
could advance research towards temporal action localization. Our dataset
website is at https://deeperaction.github.io/fineaction/.
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