# Learning Class-Specific Features with Class Regularization for Videos
## 5 Conclusions  
We have introduced _Class Regularization_, a method that focuses on class-specific features rather than treating each convolution kernel as class-agnostic. _Class Regularization_ allows the network to strengthen or weaken layer activations based on the batch data. The method can be added to any layer or block of convolutions in pretrained models. It is lightweight, as the class weights from the prediction layer are shared throughout _Class Regularization_ blocks. To avoid the vanishing gradient problem and the possibility of negatively influencing activations, the weights are normalized between a range given an _affection rate_ value.  
We evaluated the proposed method on three benchmark datasets: Kinetics-400, UCF-101, and HMDB-51; and three models: ResNet101, Wide ResNet50, and I3D. We consistently show improvement when using _Class Regularization_, with a performance gain of up to 2.45%. The achieved improvements were done with minimal additional computational cost over the original architectures. We also perform a statistical significance test to demonstrate that the outcomes of the different models are indeed based on the additional regularization, rather than being the result of incidental sampling.  
_Class Regularization_ can also aid in improving the explainability of 3D-CNNs. Qualitative visualizations reveal which spatiotemporal features are strongly correlated with specific classes. Such analyses can be made for specific layers and, as such, provide insight into the discriminative patterns that specific features represent.  
Future works based on _Class Regularization_ should aim towards addressing the feature variations of actions within the same classes, with a greater focus towards the temporal domain. The inclusion of global information and the calibration of local spatiotemporal patterns based on such information, as with _Squeeze and Recursion_ blocks [26], shows a promising direction towards creating spatiotemporal features that can better treat the variations of human actions and interactions.  
\begin{table}
\begin{tabular}{c c c c} \multicolumn{3}{c}{**a** ResNet101-3D} & \multicolumn{3}{c}{**b** Wide-ResNet50-3D} \\ \hline \hline  & \(Rg\) (+) & \(Rg\) (-) \\ \hline \(\
eg Rg\) (+) & 773 / 924 & 23 / 35 \\ \hline \(\
eg Rg\) (-) & 36 / 47 & 83 / 527 \\ \hline \multicolumn{3}{c}{**d** ResNet101-3D (over different depths)} \\ \hline \hline  & \(Rg^{2}\) (+) & \(Rg^{2}\) (-) \\ \hline \(Rg^{1}\) (+) & 18 / 30 & 0 / 2 \\ \hline \(Rg^{1}\) (-) & 35 / 89 & 861 / 1411 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c} \multicolumn{3}{c}{**b** Wide-ResNet50-3D} \\ \hline \hline  & \(Rg\) (+) & \(Rg\) (-) \\ \hline \(\
eg Rg\) (+) & 764 / 895 & 34 / 49 \\ \hline \(\
eg Rg\) (-) & 51 / 68 & 65 / 518 \\ \hline \multicolumn{3}{c}{**e** ResNet101-3D (over different depths)} \\ \hline \hline  & \(Rg^{3}\) (+) & \(Rg^{3}\) (-) \\ \hline \hline \(Rg^{3}\) (-) & 37 / 38 & 95 / 559 \\ \hline \hline \end{tabular}
\end{table}
Table 4: [PERSON]’s statistical significance test on the first split of UCF-101 and HMDB-51. Results with and without regularization for **(a)** ResNet101, **(b)** Wide-ResNet, and **(c)** I3D models. **(d–f)** present results for class-regularized ResNet101 across different blocks in the architecture.  
**Author Contributions:** [PERSON] was responsible for the methodology formalization. He has performed the software implementation and the experimentation. He is also the main contributor for the article. [PERSON] took part in the conceptualization of the work and edited the article. He also provided the funding for the work. [PERSON] supervised the work. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research is supported by the Netherlands Organization for Scientific Research (NWO) with a TOP-C2 grant for \"Automatic recognition of bodily interactions\" (ARBITER).  
**Conflicts of Interest:** The authors declare no conflict of interest.