Americas, Special Report, World

[ms-marvelesold]

Extensive experiments demonstrate that learning 3D CNN via our MUFI framework on a union of four large-scale video datasets plus two image datasets leads to superior capability of video representation. The pre-learnt 3D CNN with MUFI also shows clear improvements over other approaches on several downstream video applications. More remarkably, MUFI achieves 98.1%/80.9% on UCF101/HMDB51 for action recognition and 101.5% in terms of CIDEr-D score on MSVD for video captioning. Deep Neural Networks have been proven to be highly effective for learning vision models on large-scale datasets. The well-trained models, on one hand, manifest impressive classification performances, and on the other, produce discriminative and generic representation for image understanding tasks. Compared to static 2D images, video has one more dimension (time) and is an information-intensive media with large variations and complexities. As a result, learning a powerful spatio-temporal video representation is yet a challenging problem. Capitalizing on the high capability of deep models, one natural way to improve video representation is to acquire more video data.




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[wangiskahn2]

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