# Human Action Representation Learning Using an Attention-Driven Residual 3 DCNN Network
## 2 Related Works
[16] have proposed the utilization of a deep multi-layer LSTM for the recursive estimation of visual attention maps. Their approach involves applying the multi-layer LSTM to RGB video frames, allowing for the computation of weighted attention maps through recursive operations. They have claimed that their proposed weighted attention maps mechanism greatly helps the model in enhancing feature representation, which turns in better performance of the model for the activity recognition task. Both the two-stream 2 DCNN and CNN + LSTM based methods use two distinct architectures for capturing spatiotemporal features in video frames. The utilization of two different networks makes these approaches computationally inefficient, thereby, increasing the overall computational complexity of the model for the activity recognition task. To alleviate the computational burden of the model, numerous studies have proposed unified end-to-end 3 DCNN approaches that encapsulate the learning objective of both spatial and temporal features through a single model. Typically, 3 DCNNs utilized the first two channel of the learnable kernels for capturing spatial features, where the last channel captures the temporal flow of the spatial features across the sequence of input data samples (i.e., video frames in case of human activity recognition). For example, [PERSON] et al. [19] have introduced a modified version of the DenseNet [24] architecture called DenseNet-3D or temporal 3 DCNN (T3D). They have achieved this by replacing 2D convolution and pooling kernels with 3D convolution and pooling kernels. Through their experiments, they have claimed that their T3D model demonstrates the potential to capture both short and long-term spatiotemporal features within video sequences. In another study, [PERSON] et al. [20] have presented a specialized variant of CNN known as long-term temporal convolution (LTC). They have extended the temporal depth of their 3 DCNN convolutional layers and reduced the receptive field of feature maps. These modifications have allowed the model to effectively learn long-term spatiotemporal patterns in video streams. To capture temporal-specific features, [PERSON] et al. [22] have proposed a multi-scale 3 DCNN called Timeception that is designed to handle significant fluctuations in the temporal dimension by accommodating different temporal extents, which helps to effectively recognize long and intricate actions. The advent of 3 DCNN concept has allowed researchers to solve the sequential learning task using unified approach instead of using two different architectures. Although, the reported 3 DCNN-based approached have shown noticeable improvement over two-stream 2 DCNN and RNN-based approaches, these models are usually over-parameterized and can be optimized in terms of task-specific parameters reduction. To address the limitations of existing 3 DCNNs for the human activity recognition task, this paper proposes a residual 3 DCNN architecture with encapsulated 3D channel and spatial attention mechanisms. The proposed DA-R3 DCNN framework uses a residual 3 DCNN architecture, where each convolution layers is stacked with a channel and spatial attention module that helps our backbone model to progressively learn salient features during training. This way, the 3D channel and spatial attention module encourages the backbone residual 3 DCNN to enhance the representation of salient information across the multiple 3D convolution layers and eliminates the contribution of sparse parameters in the learning process. By eliminating the sparsity of parameter space, the proposed framework learns robust features while having a small parameters space.