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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 © condensing the input video to one frame as early fusion.




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