  Convolutional Neural Networks with 3D kernels (3D-CNNs) currently achieve
state-of-the-art results in video recognition tasks due to their supremacy in
extracting spatiotemporal features within video frames. There have been many
successful 3D-CNN architectures surpassing the state-of-the-art results
successively. However, nearly all of them are designed to operate offline
creating several serious handicaps during online operation. Firstly,
conventional 3D-CNNs are not dynamic since their output features represent the
complete input clip instead of the most recent frame in the clip. Secondly,
they are not temporal resolution-preserving due to their inherent temporal
downsampling. Lastly, 3D-CNNs are constrained to be used with fixed temporal
input size limiting their flexibility. In order to address these drawbacks, we
propose dissected 3D-CNNs, where the intermediate volumes of the network are
dissected and propagated over depth (time) dimension for future calculations,
substantially reducing the number of computations at online operation. For
action classification, the dissected version of ResNet models performs 77-90%
fewer computations at online operation while achieving ~5% better
classification accuracy on the Kinetics-600 dataset than conventional 3D-ResNet
models. Moreover, the advantages of dissected 3D-CNNs are demonstrated by
deploying our approach onto several vision tasks, which consistently improved
the performance.
