skill_benchmark

Cross-view Referenced Skill Assessment with Rank-aware Attention Network

This is the benchmark code and annotations of the "Cross-view Referenced Skill Assessment Benchmark". The implementation is based on Rank-aware Attention Network from the paper 'The Pros and Cons: Rank-aware Temporal Attention for Skill Determination in Long Videos' [Paper Link].

Label explaination

We provide explaination for each line in our label file train_split_skill.txt and val_split_skill.txt.

For example, 18_VID1_7242_7427 18_VID2_290_455 means:

The left sample shows higher skill level than the right sample.

VID1 and VID2: raw video file (25 fps)

7242_7427: from the 7242th frame to the 7427th frame

18: action

Action list:

06: Peeling
13: Cutting into chunks
14: Slicing into strips
15: Chopping into pieces
18: Egg cracking
20: Stir-fry

Note that the IDs in this action list is irrelevant to any other parts of the dataset.

Training and Validation Code

python train.py

Data

Please download the extracted i3d features for Cross-view Referenced Skill Assessment form this Google Drive link.

Acknowledgement

This code is based on RAAN. We thank the authors for their efforts.

If you have any questions, feel free to contact Mingfang at mfzhang iis.u-tokyo.ac.jp