# HiTIM: Hierarchical Task Information Mining for Few-Shot Action Recognition
## 4 Experiments
### Ablation Study
\(\mathrm{K}^{\mathrm{inter}}\) extracts task-related features by exploring the differences between tasks, whereas \(\mathrm{K}^{\mathrm{intra}}\) enhances the self-information and mutual information of features within the task. The experimental results indicated that the combination of \(\mathrm{K}^{\mathrm{inter}}\) and \(Kintra\) led to significantly better performance than the use of \(\mathrm{K}^{\mathrm{intra}}\) or \(\mathrm{K}^{\mathrm{inter}}\) alone. This is attributed to the fact that \(\mathrm{K}^{\mathrm{inter}}\) generates task-specific features, and \(\mathrm{K}^{\mathrm{intra}}\) enhances them. The joint use of these two modules resulted in the extraction of more discriminative features, improving the recognition performance. Results for D3D, compared with HiTIM* + \(\mathrm{K}^{\mathrm{inter}}\)+\(\mathrm{K}^{\mathrm{intra}}\), HiTIM achieved recognition-accuracy improvements of 0.74% and 0.99% for the HMDB51 and UCF101, respectively. The D3D, with added offsets for each sampling point in the convolution kernel, increases the convolution receptive field and enhances the modeling capability. The experimental results demonstrate that the use of D3D helps \(\mathrm{K}^{\mathrm{inter}}\) to mine task-related knowledge, further demonstrating the effectiveness of \(\mathrm{K}^{\mathrm{inter}}\). To investigate the effects of the proposed metric module and its two branches (STM and CM) on the results, we conducted ablation experiments on the matching module. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Setting** & **HMDB51** & **UCF101** \\ \hline HiTIM* & \(44.61\pm 0.92\) & \(63.95\pm 0.64\) \\ HiTIM* + \(\mathrm{K}^{\mathrm{inter}}\) & \(46.02\pm 0.81\) & \(65.06\pm 0.50\) \\ HiTIM* + \(\mathrm{K}^{\mathrm{inter}}\) & \(45.28\pm 0.86\) & \(64.81\pm 0.42\) \\ HiTIM* + \(\mathrm{K}^{\mathrm{inter}}\) + \(\mathrm{K}^{\mathrm{intra}}\) & \(46.18\pm 0.46\) & \(65.45\pm 0.38\) \\ HiTIM & \(46.92\pm 0.42\) & \(66.54\pm 0.41\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation study of \(\mathrm{K}^{\mathrm{inter}}\) and \(\mathrm{K}^{\mathrm{intra}}\) under 5-way 1-shot setting on the HMDB51 and UCF101. Figure 8: \(\mathrm{tSNE}\) plots of visual features for the first five random classes in HMDB51. (**a**) Task-agnostic feature space without \(\mathrm{K}^{\mathrm{inter}}\). (**b**) Task-related feature space using \(\mathrm{K}^{\mathrm{inter}}\). The results of the ablation study of STM and CM are shown in Table 5. Results for STM, compared with the cosine distance, the recognition accuracy of STM was 5.45% and 6.54% higher for the HMDB51 and UCF101, respectively. STM enhances the importance of key regions by exploring the correlation between spatiotemporal feature points inside the features. The experimental results indicated that the STM significantly increased recognition accuracy, suggesting that key regions of features largely determine the accuracy of matching. Results for CM, compared with the cosine distance, the recognition accuracy of CM was 5.13% and 6.14% higher for the HMDB51 and UCF101, respectively. The CM explores the correlation between the query-set and support-set features, assigning lager weights to the key parts that are correlated. The experimental results indicated that CM significantly increased recognition accuracy, suggesting that the mutual information between features influences the accuracy of matching. Results for STM and CM, the experimental results indicated that using both STM and CM increased the recognition accuracy compared with using STM or CM alone, confirming the effectiveness of the proposed matching module.