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- W4221008990 abstract "This paper endeavors to leverage spatio-temporal visual cues to improve video-based action detection. As a result, a NOn-Local Action detector based on anchor-free called NOLA is proposed, which is built off a recent moving center detector (MOC) and further extends it by efficiently aggregating long-range spatio-temporal information. In detail, a significantly efficient spatio-temporal motion-aware non-local block is explored to provide global motion contexts for the entire predictive branches of MOC. This byproduct can make the large batch samples run on a resource limited device. Besides, a light-weighted data augmentation method termed clip augmentation designed for video-based tasks is proposed, which serves to improve the generalization ability of the detector with economical scale-and-addition operation. NOLA works with two above schemes in real-time as well. Experiments on two benchmark datasets show that NOLA significantly exceeds MOC. Compared to other existing methods,, NOLA reaches the state-of-the-art, in terms of video-level mean of average precision (video mAP)." @default.
- W4221008990 created "2022-04-03" @default.
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- W4221008990 date "2022-03-05" @default.
- W4221008990 modified "2023-09-23" @default.
- W4221008990 title "Joint motion context and clip augmentation for spatio-temporal action detection" @default.
- W4221008990 doi "https://doi.org/10.1117/12.2623446" @default.
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