# Human Action Representation Learning Using an Attention-Driven Residual 3 DCNN Network
## 3 Proposed DA-R3 DCNN Human Activity Recognition Framework
### DA-R3 DCNN Architecture  
In this work, we propose a residual 3 DCNN model coupled with dual-channel spatial attention mechanism. The proposed DA-R3 DCNN network consists of eight 3D convolutional + batch normalization layers, four 3D max pooling layers, three residual blocks, and four dual-attention modules. The formation of convolution layers in our proposed DA-R3 DCNN model is determined through empirical assessments. It is important to note that we maintained a consistent number of filters across all standard 3D convolution and residual 3D convolution layers, with a fixed value of 128 kernels per layer. Furthermore, all 3D convolution layers, including residual 3D convolution layers, are coupled with batch normalization layers. The architectural details of the proposed DA-R3 DCNN model are given in Table 1. As given in Table 1, the first 3D convolutional layer operates 128 kernels of size \(3\times 3\times 3\) on input frames, which are then down-sampled by the first 3D max pooling layer having a kernel size of \(3\times 3\times 3\). The second 3D convolution layer operates 128 kernels of size \(3\times 3\times 3\) on the output feature maps from the first 3D max pooling layer. The convoluted feature maps are then operated by the first attention module that computes channel and spatial attention in input feature maps from the second 3D convolution layer, followed by the first residual block enhancing feature representations using residual convolution connection. The output features maps from the first residual block are then down-sampled by the second 3D max pooling layer, followed by two consecutive 3D convolution layers (i.e., third and fourth 3D convolution layers) which operate 128 kernels of size \(3\times 3\times 3\) on the output feature maps from the second 3D max pooling layer. The convoluted feature maps are then operated by the second attention module, followed by the second residual block. The output feature maps from the second residual block are then down-sampled by the third 3D max pooling layer having kernel size of \(3\times 3\times 3\).  
The intermediate pooled feature maps from the third 3D max pooling layer are then operated by two consecutive 3D convolution layers (i.e., fifth and sixth 3D convolution layers) using 128 kernels of size \(3\times 3\times 3\). The convoluted feature maps from the fifth and sixth 3D convolution layers are then operated by the third attention module, followed by the third residual block. The resultant feature maps from the third residual block are further down-sampled by the fourth 3D max pooling layer having kernel size of \(3\times 3\times 3\). The down-sampled feature maps form fourth 3D max pooling layer are further convoluted by the seventh and eighth 3D convolution layers, having 128 kernels of size \(3\times 3\times 3\). The output convoluted feature maps from the seventh and eighth 3D convolution layers are then operated by the fourth attention module. The feature maps generated by the fourth attention module are further improved by passing them through the fourth residual block. Subsequently, these feature maps are down-sampled using the fifth 3D max pooling layer. The pooled feature maps are then converted to 1D (i.e., \(1\times n\) size, where \(n\) represents the number of feature values) latent representation by 3D global average pooling layer. The resultant 1D feature values are then operated by 2 consecutive fully connected layers (i.e., FC1 and FC2 layers) having dimensions of \(1\times 512\). Finally, the output (i.e., logits having negative and positive values) of FC1 and FC2 layers are passed to softmax layer which converts it to final probabilities (values between 0 and 1).  
Figure 1: The graphical abstract of our proposed DA-R3 DCNN network architecture.