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One-Shot Action Localization by Learning Sequence Matching Network
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Learning based temporal action localization methods require vast amounts of training data. However, such largescale video datasets, which are expected to capture the dynamics of every action category, are not only very expensive to acquire but are also not practical simply because there exists an uncountable number of action classes. This poses a critical restriction to the current methods when the training samples are few and rare (e.g. when the target action classes are not present in the
doi:10.1109/cvpr.2018.00157
dblp:conf/cvpr/YangHP18
fatcat:3wjwgzpulvgh5adsm3b4xx37jq