# HiTIM: Hierarchical Task Information Mining for Few-Shot Action Recognition
## 4 Experiments
### Datasets and Experimental Setups  
Datasets: HMDB51 [28], UCF101 [27], and Kinetics [3] are benchmark datasets for evaluating the performance of action recognition algorithms and cover various types of actions including general daily activities and sports. The HMDB51 dataset contains 51 categories, with a total of 6849 videos. Each category contains an average of 137 video clips, and the length of the video clips varies from 17 to 266 frames, with an average of 26 frames. UCF101 contains 101 categories, with a total of 13,320 videos. Each category contains an average of 133 video clips, and the length of the video clips varies from 17 to 441 frames, with an average of 47 frames. Kinetics contains 100 categories, with a total of 40,000 videos. Each category contains an average of approximately 400 video clips, and the average length of the video clips is approximately 250 frames. These three datasets use video-level annotations and each video contains only one category of action. The training and testing sets of the model are non-overlapping in terms of categories, implying that there are differences between the source domain used for training and the target domain used for testing. Thus, the recognition accuracy on the testing set reflects the generalization ability of the model. Implementation details: As described in the previous section, to make a fair comparison with other SOTA methods, we adopted two different embeddings: 3D convolution and ResNet-50. The 3D convolution was set up the same as in other comparative methods [11; 15; 17], without importing pre-trained parameters. The size of the convolution kernel is 3 \(\times\) 3 \(\times\) 3, and the output channels are 64. For ResNet-50, we imported the pre-trained parameters on the ImageNet dataset as in comparative methods [20; 23; 24; 37]. We replaced the last average pooling and fully connected layers with a 2D convolution in ResNet-50, and the output channel dimension was reduced from 2048 to 64, ensuring that the output channel number of the two different embeddings was the same. For dividing the data into the training, validation, and testing sets, we followed the setting proposed by ARN [11]. For UCF101, the 101 categories were divided into 70 classes for training, 10 classes for validation, and 21 classes for testing. For HMDB51, the 51 categories  
Figure 6: Sample videos of HMDB51, UCF101, and Kinetics. These three datasets consist of behaviors in various domains such as sports, music, and daily life. The scenes are complex and realistic, making them three benchmark datasets with video-level annotations. were divided into 31 classes for training, 10 classes for validation, and 10 classes for testing. For Kinetics, the 100 categories were divided into 64 classes for training, 12 classes for validation, and 24 classes for testing. We randomly sampled a continuous sequence of 16 frames per video and performed random cropping and normalization on each frame of the video segments. The video frame cropping area for UCF101, HMDB51 and Kinetics was 224 \(\times\) 224, 112 \(\times\) 112, and 224 \(\times\) 224, respectively. In the training phase, we used the ADAM optimizer [47] with an initial learning rate of 0.001; the learning rate decreased by 25% after every 20,000 iterations. After every 5000 iterations, we randomly generated 600 tasks from the validation set, tested the average recognition accuracy for these tasks, and determined the model with the highest recognition accuracy. In the inference phase, we randomly generated 10,000 tasks from the test set and calculated the average recognition accuracy with 95% confidence for these tasks. For many-shot classifications, e.g., 5-shot, we followed the prototypical networks [33], and calculated the mean features of the support videos in each class as the prototypes, and classified the query videos according to their distances against the prototypes.