## Experiments
### Results and Discussion
#### 4.3.2 NTU60 and NTU120 Datasets  
Despite the large number of samples in this dataset, we performed the data augmentation due to the satisfactory performance we achieved with this on the SBU dataset. Reproducing the compared works on our machine, we find that the recognition accuracy of interaction actions is similar to the average accuracy of all actions. This phenomenon indicates that most methods do not fully use the interaction information in the interaction actions.  
We report the interaction recognition accuracy on NTU60 and NTU120 in Table 3, where we classify the baseline methods into LSTM-based, CNN-based, GCN-based, and attention-based methods. From the table, we can see that the proposed method achieves the best performance of 94.1% and 96.8% under the two evaluation metrics of NTU60, respectively. Most recent work is based on graph convolution or attention network, where they rely not only on manual data representation methods, but also explore implicit dependencies between joints through a data-driven approach. Unlike these approaches, which explore the rich dependency information between joints within a target, we take the understanding of the whole scene and the interaction information between targets as the key information to explore. This is because it is the lack of representation of interaction information that makes it difficult for single-subject action recognition methods to perform well in multi-subjects action settings. However, in addition to predefining some edges in the graph representation, we also share with these methods the learning of dynamic joint dependencies through a data-driven approach. This also brings our approach one step closer and shows better performance. For the 11 interaction action classes in this dataset, our JointContrast respectively achieves 94.1% and 96.8% recognition accuracy under Cross-Subject and Cross-View evaluation metrics, which is the best among the baseline methods. In addition to our method itself taking into account the discriminative features required for interaction recognition (including: intra-subject spatial structure information, temporal information, and inter-subjects interaction information), our pre-training with fine-tuning strategy also plays a great help. We will focus on this in our ablation experiments.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **acc** \\ \hline CFDM [21] & 89.4\% \\ ST-LSTM [1] & 93.3\% \\ Co-occurrence LSTM [43] & 90.4\% \\ VA-LSTM [44] & 97.2\% \\
2 sGCA-LSTM [45] & 94.9\% \\ SGCConv [46] & 94.0\% \\ \hline LSTM-IRN [4] & 98.2\% \\ JointContrast (ours) & 98.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The interaction recognition accuracy on the SBU dataset, accmeans recognition accuracy.  
As an extended version of the NTU60 dataset, the skeletal data in NTU120 are similar to NTU60 in terms of data form and distribution, but with the increase in the number of classes of actions and samples, the inter-class similarity and intra-class diversity have changed, making the dataset more challenging and posing a greater challenge to the accurate prediction of the models. From the recognition accuracy results presented in Table 3, we can see that the recognition accuracy of all methods decreases under different evaluation metrics, which is in line with expectations. However, again, we can see that the proposed method is the best performing among the compared methods in terms of both Cross-Subject and Cross-View evaluation metrics, achieving 88.2% and 88.9%, respectively.  
In Figure 4, we show the comparison of the recognition accuracy of our method with ST-TR and MS-G3D methods under the Cross-Subject evaluation accuracy on the NTU60 dataset. We are able to see that the recognition accuracy of our method is better than both ST-TR and MF-F3D in 11 interaction action classes, due to the modeling of spatial, temporal and interaction information and the exploration of the co-occurrence between these features in our method.