  3D Convolutional Neural Network (3D CNN) captures spatial and temporal
information on 3D data such as video sequences. However, due to the convolution
and pooling mechanism, the information loss seems unavoidable. To improve the
visual explanations and classification in 3D CNN, we propose two approaches; i)
aggregate layer-wise global to local (global-local) discrete gradients using
trained 3DResNext network, and ii) implement attention gating network to
improve the accuracy of the action recognition. The proposed approach intends
to show the usefulness of every layer termed as global-local attention in 3D
CNN via visual attribution, weakly-supervised action localization, and action
recognition. Firstly, the 3DResNext is trained and applied for action
classification using backpropagation concerning the maximum predicted class.
The gradients and activations of every layer are then up-sampled. Later,
aggregation is used to produce more nuanced attention, which points out the
most critical part of the predicted class's input videos. We use contour
thresholding of final attention for final localization. We evaluate spatial and
temporal action localization in trimmed videos using fine-grained visual
explanation via 3DCam. Experimental results show that the proposed approach
produces informative visual explanations and discriminative attention.
Furthermore, the action recognition via attention gating on each layer produces
better classification results than the baseline model.
