# HiTIM: Hierarchical Task Information Mining for Few-Shot Action Recognition
## 4 Experiments
### Ablation Study  
To verify the effectiveness of the proposed modules in HiTIM, i.e., \(\mathrm{K}^{\mathrm{inter}}\), \(\mathrm{K}^{\mathrm{intra}}\), STM, and CM, we conducted ablation experiments under the 5-way 1-shot task settings of the UCF101 and HMDB51, with the 3D CNN embedding without pre-training. To investigate the effectiveness of \(\mathrm{K}^{\mathrm{inter}}\) and \(\mathrm{K}^{\mathrm{intra}}\), we introduced a comparative model called HiTIM*. The model is based on HiTIM, but with \(\mathrm{K}^{\mathrm{inter}}\) and \(\mathrm{K}^{\mathrm{intra}}\) removed, while the inputs and outputs remain the same, as shown in Table 4. In \(\mathrm{K}^{\mathrm{inter}}\), D3D is replaced with 3D convolution. The results of the ablation study of \(\mathrm{K}^{\mathrm{inter}}\) and \(\mathrm{K}^{\mathrm{intra}}\) are shown in Table 4. Results for \(\mathrm{K}^{\mathrm{inter}}\), compared to HiTIM*, HiTIM*+ \(\mathrm{K}^{\mathrm{inter}}\) achieved a recognition-accuracy improvement of 1.41% and 1.11% for HMDB51 and UCF101, respectively. The additional parameters introduced by \(\mathrm{K}^{\mathrm{inter}}\) implicitly capture the knowledge of inter-task differences in the few-shot scenario. As shown in Figure 8, in the task-related feature space established by \(\mathrm{K}^{\mathrm{inter}}\), the intra-class distance of video features from five random categories was reduced, while the inter-class distance was increased. The experimental results demonstrated that \(\mathrm{K}^{\mathrm{inter}}\) could construct task-related feature space and increase recognition accuracy, confirming its effec \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
2D Prototypical Networks & 54.2 & 68.4 & 74.0 & 89.6 \\
[33] & 54.5 & 68.0 & 79.9 & 88.9 \\ OTAM [20] & 53.1 & 75.6 & 78.2 & **96.1** \\ PAL [37] & 60.9 & 75.8 & **85.3** & 95.2 \\ TA\({}^{2}\)N [48] & 59.7 & 73.9 & 81.9 & 95.1 \\ HyRSM [24] & 60.3 & 76.0 & 83.9 & 94.7 \\ HiTIM & **61.0** & **77.4** & 84.6 & 94.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The 5-way 1-shot and 5-way 5-shot few-shot recognition accuracy results using 2D CNN embedding pre-trained on ImageNet on the HMDB51 and UCF101 datasets. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **5-Way 1-Shot** & **5-Way 5-Shot** \\ \hline OTAM [20] & 73.0 & 85.8 \\ TRX [23] & 63.6 & 85.9 \\ PAL [37] & 74.2 & **87.1** \\ TA\({}^{2}\)N [48] & 73.0 & 85.8 \\ HyRSM [24] & 73.7 & 86.1 \\ HiTIM & **74.3** & 86.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The 5-way 1-shot and 5-way 5-shot few-shot recognition accuracy results using 2D CNN embedding pre-trained on ImageNet on the Kinetics dataset. tiveness. Results for \(\mathrm{K}^{\mathrm{intra}}\), compared with HiTIM*, HiTIM*+\(\mathrm{K}^{\mathrm{intra}}\) achieved recognition-accuracy improvement of 0.67% and 0.86% for HMDB51 and UCF101, respectively. The parameters of \(\mathrm{K}^{\mathrm{intra}}\) contain implicit feature information, which can be used to mine the key parts of the attention matrix. The experimental results indicated that the attention mechanism had a limited feature enhancement effect, and \(\mathrm{K}^{\mathrm{intra}}\) enhanced the distribution of key feature information and the correlation between features, thereby reducing the degree of redundancy of task-specific features and aligning them. Results for \(\mathrm{K}^{\mathrm{inter}}\) + \(\mathrm{K}^{\mathrm{intra}}\), compared to HiTIM* + \(\mathrm{K}^{\mathrm{inter}}\) and HiTIM* + \(\mathrm{K}^{\mathrm{intra}}\), HiTIM achieved recognition-accuracy improvements of 0.90% and 1.64% in recognition accuracy for HMDB51, and improvements of 0.48% and 0.73% for UCF101, respectively.