# Dilated Multi-Temporal Modeling for Action Recognition
## 4 Results
### Main Results
#### 4.3.3 Results8960/432 on Kinetics400  
Table 3 shows the comparison with the state-of-the-art results for our DMTNet using ResNet-50 as a backbone with 8- and 16-frame input samplings and testing on the validation set. In comparison to the baseline TANet [27], we argue that we obtain a competitive accuracy without higher improvement because the activities in Kinetics400 are more easily inferred from a single frame, unlike the FingGym99 and 5 thV1 datasets that have a stronger dependency on temporal modeling. Additionally, we used ResNet-50 as the backbone for our DMTNet, which may have limited the performance compared to deeper network architectures such as ResNet-101 and ResNet-152. However, on the FineGym99 and 5 thV1 datasets, which have a stronger dependency on temporal modeling, ResNet-50 as the backbone for our DMTNet is able to achieve significant performance improvements as well.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Methods** & **Backbones** & **Frames** & **FLOPs** & **Top-1** & **Top-5** \\ \hline TSN-RGB [23] & BNInception & 8 f & 16 G & 19.5 & - \\ TRN-Multiscale [32] & BNInception & 8 f & 33 G & 34.4 & - \\ S3D-G [6] & Inception & 64 f & 71 G & 48.2 & 78.7 \\ ECO [35] & BNIncep + Res18 & 16 f & 64 G & 41.6 & - \\ ECO\({}_{En}\)Lite [35] & BNIncep + Res18 & 92 f & 267 G & 46.4 & - \\ \hline TSN [23] & ResNet50 & 8 f & 33 G & 19.7 & 46.6 \\ I3D [15] & ResNet50 & 32 f \(\times\) 2 & 306 G & 41.6 & 72.2 \\ NL I3D [29] & ResNet50 & 32 f \(\times\) 2 & 334 G & 44.4 & 76.0 \\ NL I3D +GCN [29] & ResNet50+GCN & 32 f \(\times\) 2 & 606 G & 46.1 & 76.8 \\ TSM [25] & ResNet50 & 8 f & 33 G & 45.6 & 74.2 \\ TSM [25] & ResNet50 & 16 f & 65 G & 47.2 & 77.1 \\ TSM\({}_{En}\)[25] & ResNet50 & 8 f + 16 f & 98 G & 49.7 & 78.5 \\ bLVNet-TAM [36] & bLResNet-50 & 16 f \(\times\) 2 & 48 G & 48.4 & 78.8 \\ GST [37] & ResNet50 & 8 f & 30 G & 47.0 & 76.1 \\ GST [37] & ResNet50 & 16 f & 59 G & 48.6 & 77.9 \\ TEINet [38] & ResNet50 & 8 f & 33 G & 47.4 & - \\ TEINet [38] & ResNet50 & 16 f & 66 G & 49.9 & - \\ TPN [10] & ResNet50 & 8 f & - & 49.0 & - \\ TANet [27] & ResNet50 & 8 f & 33 G & 47.3 & 75.8 \\ TANet [27] & ResNet50 & 16 f & 66 G & 47.6 & 77.7 \\ STFG-Net (TSM) [33] & ResNet-50 & 8 f \(\times\) 2 & 35.9 G & 49.3 & 77.8 \\ STFG-Net (TSM) [33] & ResNet-50 & 16 f \(\times\) 2 & 69.4 G & 50.7 & 78.8 \\ TEFE [34] & ResNet-50 & 8 f & 90 G & 46.7 & 75.3 \\ TEFE [34] & ResNet-50 & 16 f & 181 G & 50.4 & 78.9 \\ \hline DMTNet & ResNet50 & 8 f & 33 G & 48.3 & 77.6 \\ DMTNet & ResNet50 & 16 f & 66 G & 49.4 & 78.4 \\ DMTNet 1 & ResNet50 & 16 f \(\times\) 2 & 86 G \(\times\) 2 & 51.0 & 79.0 \\ DMTNet 2 & ResNet50 & 8 f \(\times\) 2 \(\times\) 3 & 43 G \(\times\) 6 & 49.9 & 78.4 \\ DMTNet 2 & ResNet50 & 16 f \(\times\) 2 \(\times\) 3 & 86 G \(\times\) 6 & 51.2 & 79.0 \\ \hline \hline \end{tabular} 1 Double sampling that uses two clips and one crop, each with a resolution of 256 \(\times\) 256, can improve accuracy. 2 Double sampling that uses two clips and three crops, each with a resolution of 256 \(\times\) 256, can improve accuracy.  
\end{table}
Table 2: Something-Something V1 results with other methods. In the evaluation, we instantiated our DMT with a ResNet50 backbone. To ensure a fair comparison, we employed an evaluation strategy using 8 and 16 frames, and we compared our method to others that use similar-scale backbone networks. The GFLOPs calculation is based on spatial resolutions of 224 \(\times\) 224 and 256 \(\times\) 256.