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

Dissected 3D CNNs: Temporal Skip Connections for Efficient Online Video Processing