# Temporal Action Detection in Untrimmed Videos from Fine to Coarse Granularity
## 4 Experiments
### Implementation Details  
Res3D training: Res3D architectures are trained on the training, validation, and background sets. We generate non-overlapped segments by uniformly sampling 16 frames. In the proposal step, for each segment of the trimmed videos in training set, we set its label as positive. For the untrimmed videos in validation set, we assign the segments of ground truth with positive labels and assign the segments which do not overlap with a ground truth with negative labels. We also assign each segment of background videos with negative label. At last, we obtain \(N_{trn}+N_{vld}\) positive samples and also randomly selected \(N_{trn}+N_{vld}\) negative samples for training. In the classification steps, we follow the similar segments generation strategy. Except when assigning label for positive segment, we explicitly indicate specific action category. In order to balance the number of training data for each category, we randomly select \(\frac{N_{trn}+N_{vld}}{K}\) negative samples for training. In the proposal step, for RGB Res3D, learning rate begins at 0.001, decreases by 1/10 every epoch, and stops at four epochs; for OF Res3D architecture, the learning rate starts at 0.01, decreases by 1/10 every five epochs, and stops at 20 epochs. In the classification steps, for RGB Res3D, learning rate begins at 0.0001, decreases by 1/10 every epoch, and stops at four epochs; for OF Res3D architecture, the learning rate starts at 0.001, decreases by 1/10 every five epochs, and stops at 20 epochs.  
Regression network training: Regression network is trained on the validation set, as the videos in the validation set of THUMOS'14 is untrimmed. We use multi-scale sliding windows to generate 50% overlapped windows with variable lengths of \(\{2,3,\dots,50\}\) segments as the training samples. We assign a positive label to a window if: (1) the window with the highest temporal Intersection over Union (tIoU) overlaps with a ground truth; or, (2) the window has tIoU larger than 0.5 with any of the ground truth. We assign a negative label to a window which has no overlap with any ground truth. During training, the learning rate and batch size are set as 0.005 and 128, respectively. The ratio of sample numbers of background to action in a batch is set to 10. \(\lambda\) is set to 2.  
LSTM training: LSTM is trained on the training, validation and background sets. We use each trimmed video in the training set and each action instance in the validation set for training. We set their labels as the specific action category and obtain \(M_{trn}+M_{vld}\) action samples. For background samples, we generate windows with 2 to 50 segments from the validation and background sets. The windows from validation set are not overlapped with any ground truth. We randomly select\(\frac{M_{tan}+M_{aid}}{K}\) background samples. During training, we set the learning rate to 0.0001, and stop the training at 90 epochs.  
For the ActivityNet1.3 dataset, we setup the experiments almost following the aforementioned implementation of THUMOS'14. The differences are that there is no background set and the videos in the training set is untrimmed. Thus, we generate the segment-level and window-level training samples from the untrimmed videos in the training and validation sets. In the training and testing phase, we also update the output number of fully-connected layer in the multi-class Res3D and LSTM of the classification step.