Multiple ways to fuse temporal information from consecutive frames using 2D pre-trained convolutions












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In Large-scale Video Classification with Convolutional Neural Networks by Karpathy et al., Can anyone explain the architecture, inputs and training process of Single Frame, Late Fusion, Early Fusion, Slow Fusion in more detail.



Here is a bit more to refer but it just explains as much explained in the paper.



I can't get how is the information fused over different stages and how is the below images should be interpreted?



Here is the pic









share









$endgroup$

















    0












    $begingroup$


    In Large-scale Video Classification with Convolutional Neural Networks by Karpathy et al., Can anyone explain the architecture, inputs and training process of Single Frame, Late Fusion, Early Fusion, Slow Fusion in more detail.



    Here is a bit more to refer but it just explains as much explained in the paper.



    I can't get how is the information fused over different stages and how is the below images should be interpreted?



    Here is the pic









    share









    $endgroup$















      0












      0








      0





      $begingroup$


      In Large-scale Video Classification with Convolutional Neural Networks by Karpathy et al., Can anyone explain the architecture, inputs and training process of Single Frame, Late Fusion, Early Fusion, Slow Fusion in more detail.



      Here is a bit more to refer but it just explains as much explained in the paper.



      I can't get how is the information fused over different stages and how is the below images should be interpreted?



      Here is the pic









      share









      $endgroup$




      In Large-scale Video Classification with Convolutional Neural Networks by Karpathy et al., Can anyone explain the architecture, inputs and training process of Single Frame, Late Fusion, Early Fusion, Slow Fusion in more detail.



      Here is a bit more to refer but it just explains as much explained in the paper.



      I can't get how is the information fused over different stages and how is the below images should be interpreted?



      Here is the pic







      machine-learning deep-learning convolution





      share












      share










      share



      share










      asked 2 mins ago









      jayjay

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