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When comparing to baseline methods that map video sequence to a single image, IFS shows superior performance. More remarkably, IFS consistently demonstrates evident improvements on image-based 2D networks and clip-based 3D networks, and achieves comparable performance with the state-of-the-art methods with less computational cost. Recently, the development of Convolutional Neural Networks (CNN) convincingly demonstrates high capability of CNN in image-domain visual recognition. 3.5% top-5 error on the ImageNet test set, which is even lower than 5.1% of the reported human-level performance. Nevertheless, it is not trivial to apply a 2D CNN for video recognition. Since video is a temporal sequence with large variations and complexities, performing 2D CNN on individual frame cannot model temporal evolution across frames. Figure 1: Modeling temporal evolution for video recognition by (a) aggregating 2D representations from sampled frames by late fusion, (b) slow fusing the input frames by 3D CNN and (c) condensing the input video to one frame as early fusion.




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