## 2 Related Works
### Action Recognition Methods
The new architecture (3D ConvNet) is able to produce robust features (C3D) that can be effectively used in a simple linear classifier for action recognition. [PERSON] et al. [33] extend a two-stream network architecture, based on inception-V1, with 3D convolutions creating a two-stream inflated 3D ConvNet (I3D). The temporal 3D ConvNet [34] extends a DenseNet architecture by introducing a new temporal layer that models variable temporal convolution kernel depths with 3D filters and 3D pooling kernels. An approach to learn video representations using neural networks with long-term temporal convolutions (LTC) is presented in [32]. Different low-level frame representations are considered, and high-quality optical flows are found to be the most relevant for robust action recognition. Standard Convolutional Neural Networks (CNN) analyze information at local neighborhood. [PERSON] et al. [40] introduced non-local operations as a generic family of building blocks for capturing long-range dependencies in action recognition videos. Experiments are performed on the Inflated 3D ConvNet showing improvements to the Kinetics dataset. [PERSON] et al. [39] demonstrated the advantages of 3D CNNs over 2D CNNs within the framework of residual learning. The 3D convolutional filters are factorized into separate spatial and temporal components. The devised R(2+1)D convolutional block (2+1-dimensional ResNet) can achieve comparable or superior results to the state-of-the-art methods. Training networks for action recognition usually requires a large amount of annotated data. In [43] a study is conducted on how to improve action recognition classification using large-scale weakly supervised pre-training. The reference model used in the experimentation is the R(2+1)D-d [39]. Results shows that notwithstanding data noise, the models significantly improve the state-of-the-art performance. Most 3 DCNN models are built upon RGB and optical flow streams and lack information about human pose. [PERSON] et al. [44] proposed a novel model that encodes multiple pose modalities within a unified 3D framework. The model, Pose-Action 3D Machine (PA3D), exploits a novel temporal pose convolution to aggregate spatial poses over frames. Building deep 3 DCNN results in expensive computational cost and memory demand. [PERSON] et al. [35] proposed a new family of Pseudo-3D (P3D) blocks to replace 2D Residual Units in ResNet achieving spatio-temporal encoding for videos. 3 DCNN-based approaches incorporate temporal information extending filter and pooling layer to work with group of frames. Approaches based on Long-Short-Term Memory networks (LSTM) process a video as an ordered sequence of frames. Each frame is fed to the network that retains information about previous frames in internal memory states. [PERSON] et al. [28] proposed a recurrent neural network that uses LSTM cells connected to the output of the underlying CNN. [PERSON] et al. [22] developed a novel recurrent convolutional architecture suitable for large-scale visual learning. The tested RNN models are directly connected to ConvNet models, and are trained to output variable length video descriptions of actions. In [31] a spatio-temporal LSTM for 3D human action recognition is proposed. The standard LSTM learning approach is extended to incorporate both spatial and temporal domains to analyze the hidden sources of action-related information within the input data over both domains concurrently. In standard LSTM approaches it is implicitly assumed that motions in videos are stationary across different spatial locations. To overcome this limitation, Lattice-LSTM [36] extends LSTM by learning independent hidden state transitions of memory cells for individual spatial locations. In [37] a network based on the recurrent neural networks with long short-term memory units is built. The model uses a spatial attention module to assign different levels of importance to different joints in a 3D skeleton. Moreover, a temporal attention module allocates different levels of attention to each frame within a sequence.