MotionSqueeze: Neural Motion Feature Learning for Video Understanding

Heeseung Kwon
Heeseung Kwon
Manjin Kim
Manjin Kim

european conference on computer vision, pp. 345-362, 2020.

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We have presented an efficient yet effective motion feature block, the MS module, that learns to generate motion features on the fly for video understanding

Abstract:

Motion plays a crucial role in understanding videos and most state-of-the-art neural models for video classification incorporate motion information typically using optical flows extracted by a separate off-the-shelf method. As the frame-by-frame optical flows require heavy computation, incorporating motion information has remained a maj...More

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