Reviews: Recurrent Space-time Graph Neural Networks

However, [A][B] use similar space-time factorization, in which separate spatial and temporal graph convolutions are performed. Considering this, the novelty of this work is weakened. More experiments on complex human-object interaction datasets, e.g., Charades, would be helpful in showing the scalability of the adopted rigid region-split scheme. It would also be helpful to compare with the existing space-time graphical modeling approaches, e.g., [B][C][33], on such datasets. There are no ablation studies concerning these two modules to shed a light on which part actually brings the performance boost. Studies for analyzing the correlation between performance and number of scales are also missing.