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ms-marvelesold
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Therefore, it is important to combine both spatial and temporal selection, as well as to improve their flexibility. Such motivated, this paper proposes a novel Action Keypoint Network (AK-Net) to integrate spatial and temporal selection for efficient video recognition. Instead of selecting some whole frames or whole patches, AK-Net uses “point” as the basic unit for extracting informative content, as illustrated in Fig. 1 (d). These points can be selected from any spatial locations within any temporal frames, therefore bringing high flexibility for temporal-spatial integration. We note that using point as the basic representation unit shares inspiration from the traditional space-time interest points (STIPs). However, the differences between STIPs and the proposed AK-Net are significant and fundamental. In contrast, AK-Net generalizes such inspiration into deep learning and conducts point-wise selection in the deep feature space. To imply these differences from STIPs, in this paper, we name these informative points as action keypoints, and name the proposed method as Action Keypoint Network (AK-Net).




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