Video Transformer for Engagement Prediction Task - PyTorch

Implementation of the Class Attention in Video Transformer

Usage

Step 1: Download model files from googledriver/baidu and put them optimal_checkpoint folder.

Googledrive: https://drive.google.com/drive/folders/1POM4AD32hrZ1gVxNQE3Q-8zAvUtcDued
Baidu: https://pan.baidu.com/s/1EPeGitXScwP20Vz4E3SSMw kbac

Step 2: run OpenFace https://github.com/TadasBaltrusaitis/OpenFace/wiki with Docker to perform face extraction on validation set (EmotiW-EP) and test set (DAiSEE).

FeatureExtraction.exe -nomask -simscale 1 -f "video1.avi" -f "video2.avi" -f "video3.avi"

Step 3: Organize datasets according to directory structure.

Engwild2020
	validation_112_scale1_frames
		2100011001_aligned
			frame_det_00_000001.bmp
			frame_det_00_000002.bmp
			......
		2100011002_aligned
			frame_det_00_000001.bmp
			frame_det_00_000002.bmp
			......
		......
DAiSEE
    DataSet
	test_112_scale1_frames
		3100011001_aligned
			frame_det_00_000001.bmp
			frame_det_00_000002.bmp
			......
		3100011002_aligned
			frame_det_00_000001.bmp
			frame_det_00_000002.bmp
			......
		......

Step 4: Run tools/test.sh to reproduce our results for two CavT models as follows.

Evaluating mse ...
mse for 0.0: 0.1107
mse for 0.33: 0.0983
mse for 0.66: 0.0119
mse for 1.0: 0.0482
Mean class mse: 0.0673
mean square error	0.0495
mse: 0.0495
Evaluating mse ...
mse for 0.0: 0.5535
mse for 0.33: 0.1770
mse for 0.66: 0.0203
mse for 1.0: 0.0396
Mean class mse: 0.1976
mean square error	0.0377
mse: 0.0377

References

Some part of the code is adapted from the VideoSwinTransformer repository https://github.com/SwinTransformer/Video-Swin-Transformer. Special thanks for their great work!