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- W2802783553 abstract "Temporal action detection in untrimmed video is an important and challenging task in computer vision. In this paper, a straightforward and efficient regression model is proposed by us to detect action instance and refine action interval in long untrimmed videos. We train a single 3D Convolutional Networks (3D ConvNets) jointly with two sibling output layers: a classification layer to predict the class label and a temporal interval regression layer to modify the temporal localization of input proposal. We also introduce an effective method to sample negative and positive proposals which are discriminative to feature extractor and classifier during training. On THUMOS 2014 dataset, our method achieves competitive performance compared with recent state-of-the-art methods." @default.
- W2802783553 created "2018-05-17" @default.
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- W2802783553 date "2018-01-01" @default.
- W2802783553 modified "2023-09-27" @default.
- W2802783553 title "Temporal Interval Regression Network for Video Action Detection" @default.
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- W2802783553 doi "https://doi.org/10.1007/978-3-319-77380-3_25" @default.
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