# DANet: Temporal Action Localization with Double Attention
## 4 Experiments and Results
### Datasets and Experimental Setups  
To assess the performance of the proposed DANet better, in this section, we adopt mean average precision (mAP) and time Intersection over Union (tIoU) as metrics, following the prior studies [9; 10]. The time Intersection over Union (tIoU) measures the degree of overlap between two temporal windows using the 1D Jaccard index. Specifically, the mAP is commonly used to assess the effectiveness of methods at various temporal intersections across all datasets. In addition, the mAP metric computes the average precision across all action categories given a tIoU threshold, while the average mAP is obtained by averaging multiple tIoUs.  
In addition, there are two datasets used in this section, namely THUMOS14 and ActivityNet1.3. The THUMOS14 and ActivityNet1.3 datasets are well-established benchmark datasets in the field of action recognition, renowned for their diverse and challenging video content. These datasets have garnered substantial recognition and have been extensively utilized in numerous prior research studies [5; 9; 10]. Specifically, the THUMOS14 dataset consists of 413 untrimmed videos and 20 action categories for our experimentation. The dataset is divided into a training set and a test set. The training set comprises 200 videos and the test set includes 213 videos, which contain 3007 and 3358 action instances, respectively. Notably, the average duration of an action instance is 5 s. To extract features from our videos, following [39], we employ a pre-trained 3D CNN network, e.g., I3D model [37]. Our process involves extracting a number of \(J\) RGB frames and a number of \(J\) optical flows from each video, and then feeding a number of 16 consecutive frames into I3D. Before the final fully connected layer, we extract 1024-dimensional RGB features and 1024-dimensional flow features, which are then merged into 2048-dimensional features and used as input of our model. In our experiment, the following hyper-parameters are used: the input dimension is set to 2048, the batch size is set to 1, and the maximum sequence length is set to 12,672. By conducting preliminary experiments, the level of the feature pyramid is set to 6, the training epoch is set to 30, and the linear warm-up epoch is set to 5 (see Section 4.3.7 for more details about warm-up). Finally, the initial learning rate is set to \(1\times 10^{-4}\), and the weight decay is set to 0.05, following a prior study [10]. We use mAP@[0.3:0.1:0.7] to evaluate our model, referring to computing the corresponding mAP at each tIoU interval from 0.3 to 0.7 with an interval of 0.1.  
ActivityNet1.3 is a vast action-oriented dataset comprising 200 activity categories and over 20,000 videos, totaling 600+ h. Following the prior studies [4; 7; 40], we train our model on the training set. Similar to THUMOS14, we utilize features extracted from the I3D model [37]. The features are extracted from non-overlapping clips of 16 frames and a stride of 16 frames. By conducting preliminary experiments, the training epoch is set to 15, the linear warm-up epoch is set to 5, the learning rate is set to \(1\times 10^{-3}\), the mini-batch size is set to 16, and the weight decay is set to \(1\times 10^{-4}\). We use mAP@[0.5:0.05:0.95] to evaluate our model, referring to computing the corresponding mAP at each tIoU interval from 0.5 to 0.95 with an interval of 0.05.  
The experiments are conducted on a workstation with a single NVIDIA GeForce RTX 3090 card, and Intel(R) Xeon(R) Gold 6254 CPU @ 3.10 GHz.