# HiTIM: Hierarchical Task Information Mining for Few-Shot Action Recognition
## 5 Discussion  
Most of the current deep-learning-based video-action-recognition methods rely heavily on large amounts of annotated data. However, in the real world, obtaining and annotating data for some categories can be challenging due to factors such as filming difficulties, collection and annotation costs, and privacy and ethical concerns. Consequently, few-shot action recognition has significant application value in areas such as video surveillance and human-computer interaction.  
We propose a hierarchical task information mining (HiTIM) method for few-shot action recognition. This method uses the K\({}^{\text{inter}}\) and the matching module with the K\({}^{\text{intra}}\). The K\({}^{\text{inter}}\) adaptively generates network parameters according to different tasks, constructs a task-related feature space, and improves model generalization. The matching module with the K\({}^{\text{intra}}\), which consists of two branches (the STM and CM), strengthens the self-information and mutual information of features. The K\({}^{\text{intra}}\) optimizes the two matching branches to enhance feature discriminability and improve matching performance. In experiments involving the HMDB51, UCF101, and Kinetics datasets, our method achieved new SOTA recognition accuracies, outperforming leading few-shot action recognition algorithms using a 3D CNN without pre-training and a 2D CNN pre-trained on ImageNet as embedding. In future work, we will try to develop improved structures and apply our approach to practical settings such as surveillance and medical applications.  
Conceptualization, P.C.; methodology, J.Y. and Y.D.; software, J.Y. and L.J.; validation, J.Y., Y.D. and R.H.; investigation, J.Y.; resources, P.C.; data curation, J.Y.; writing--original draft preparation, L.J. and J.Y.; writing--review and editing, P.C. and R.H.; visualization, L.J. and J.Y.; project administration, P.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation (Grant U1909203 and 62206250), and Zhejiang Provincial Natural Science Foundation of China (Grant LY19F020032 and LQ22F020007).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Match** & **HMDB51** & **UCF101** \\ \hline Cosine & \(40.28\pm 0.95\) & \(58.71\pm 1.12\) \\ STM & \(45.73\pm 0.68\) & \(65.37\pm 0.52\) \\ CM & \(45.41\pm 0.56\) & \(64.81\pm 0.43\) \\ STM + CM & \(46.92\pm 0.42\) & \(66.54\pm 0.41\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study of STM and CM under 5-way 1-shot setting on the HMDB51 and UCF101 datasets.  
Data Availability Statement: The UCF101, HMDB51, and Kinetics used in this study are available at [[https://tensorflow.google.cn/datasets/catalog/ucf101](https://tensorflow.google.cn/datasets/catalog/ucf101)]([https://tensorflow.google.cn/datasets/catalog/ucf101](https://tensorflow.google.cn/datasets/catalog/ucf101)) and [[https://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database](https://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database)]([https://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database](https://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database)), [[https://www.deepmind.com/open-source/kinetics](https://www.deepmind.com/open-source/kinetics)]([https://www.deepmind.com/open-source/kinetics](https://www.deepmind.com/open-source/kinetics)), respectively, accessed on 13 April 2023.