# HiTIM: Hierarchical Task Information Mining for Few-Shot Action Recognition
## 4 Experiments
### Comparative Experiments
#### 4.2.2 Using 2D CNN Embedding Pre-Trained on ImageNet  
Owing to the effective training of complex 2D CNN embedding, such as ResNet-50, on the large-scale ImageNet dataset containing over one million images, these embeddings have greatly improved the performance on tasks such as few-shot action recognition. This is due to the rich prior knowledge that ImageNet provides. In this section, we replace the embedding with ResNet-50 and compare the model with SOTA algorithms. We conduct comparison experiments on the Kinetics dataset to increase the persuasiveness of our results.  
As shown in Tables 2 and 3, in the 5-way 1-shot and 5-way 5-shot experiments on the HMDB51, and in the 5-way 1-shot experiments on Kinetics, HiTIM achieved recognition accuracies of 61.0%, 74.3% and 77.4%, respectively, which were higher than those of the other SOTA methods. In experiments with other settings, the performance of our proposed method is comparable to that of the best performing SOTA method, indicating that HiTIM is competitive in terms of performance compared to these methods. This may because, although ResNet-50 extracts high-quality images, these temporal alignment methods [20; 23; 24; 37] lack discriminative information across tasks, and isolating image feature extraction also loses motion information. HiTIM incorporates inter-task information  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & \multicolumn{2}{c}{**HMDB51**} & \multicolumn{2}{c}{**UCF101**} \\  & **5-Way 1-Shot** & **5-Way 5-Shot** & **5-Way 1-Shot** & **5-Way 5-Shot** \\ \hline
3D Prototypical & & & & \\ Networks [33] & \(38.05\pm 1.02\) & \(53.15\pm 0.73\) & \(57.05\pm 1.02\) & \(78.25\pm 0.73\) \\
3D Relation Network [35] & \(38.23\pm 0.97\) & \(53.17\pm 0.86\) & \(58.21\pm 1.02\) & \(78.35\pm 0.72\) \\
3D SoSN [17] & \(40.83\pm 0.96\) & \(55.18\pm 0.86\) & \(62.57\pm 1.03\) & \(81.51\pm 0.73\) \\ ARN [11] & \(42.41\pm 0.99\) & \(56.81\pm 0.87\) & \(64.48\pm 1.06\) & \(82.37\pm 0.72\) \\ ARN [11] + SeSA-rotation & \(45.15\pm 0.96\) & \(60.56\pm 0.86\) & \(66.32\pm 0.99\) & \(83.12\pm 0.70\) \\ MR [15] & \(42.62\pm 0.52\) & \(58.39\pm 0.84\) & \(65.17\pm 0.38\) & \(83.51\pm 0.65\) \\ HiTIM & \(\mathbf{46.92\pm 0.42}\) & \(\mathbf{61.74\pm 0.67}\) & \(\mathbf{66.54\pm 0.51}\) & \(\mathbf{84.71\pm 0.53}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The 5-way 1-shot and 5-way 5-shot few-shot recognition accuracy results using 3D CNN embedding without pre-training on the HMDB51 and UCF101 datasets.  
into the prior knowledge of ResNet-50 embedding and explores self-information in the spatiotemporal domain as well as correlations between features.  
PAL [37] outperforms our method in the 5-way 1-shot experiments on UCF101 and 5-way 5-shot experiment on Kinetics, possibly because it incorporates query-set information when building prototypes, while our approach treats the query set as completely unknown and our method is more in line with the task setting. Although TRX [23] performs better than our method in the 5-way 5-shot experiments on UCF101, it is affected by the number of input frames when computing the combined frame tuples, while our method handles video-level features, which are more flexible to set the number of input frames. Overall, our method achieves new SOTA performance in more settings than other SOTA algorithms and can be applied flexibly to different embeddings, demonstrating the advantages of our approach.