  This paper proposes a method for visually explaining the decision-making
process of video recognition networks with a temporal extension of occlusion
sensitivity analysis, called Adaptive Occlusion Sensitivity Analysis (AOSA).
The key idea here is to occlude a specific volume of data by a 3D mask in an
input 3D temporal-spatial data space and then measure the change degree in the
output score. The occluded volume data that produces a larger change degree is
regarded as a more critical element for classification. However, while the
occlusion sensitivity analysis is commonly used to analyze single image
classification, applying this idea to video classification is not so
straightforward as a simple fixed cuboid cannot deal with complicated motions.
To solve this issue, we adaptively set the shape of a 3D occlusion mask while
referring to motions. Our flexible mask adaptation is performed by considering
the temporal continuity and spatial co-occurrence of the optical flows
extracted from the input video data. We further propose a novel method to
reduce the computational cost of the proposed method with the first-order
approximation of the output score with respect to an input video. We
demonstrate the effectiveness of our method through various and extensive
comparisons with the conventional methods in terms of the deletion/insertion
metric and the pointing metric on the UCF101 dataset and the Kinetics-400 and
700 datasets.
