Awesome

Hierarchical Modeling for Task-Recognition and Action Segmentation in Weakly-Labeled Instructional Videos

Here is the code for our WACV 2022 paper : https://arxiv.org/pdf/2110.05697.pdf

Main Software Requirements:

PyTorch 1.1

Python 3.6

numpy among others

Ubuntu 16

Instructions to reproduce the task recognition results on the Beakfast dataset using I3D and iDT features:

Make sure you have the following folder directories

|data
	| i3d

		| features

	| idt 

		| features

	| groundTruth

	| transcripts

|logs

|Visualization

|utils_folder 

Data Preparation

0-0- Download the pre-computed I3D features from the third party link used in [1]: https://zenodo.org/record/3625992#.X7vj8axKjCJ

0-1- Extract the content of the "/breakfast/features/" inside the defined "/data/i3d/features/" directory.

0-2- Download the pre-computed iDT from the third party link used in [2]: https://uni-bonn.sciebo.de/s/wOxTiWe5kfeY4Vd

0-3- Extract the content of the "data/features/" inside our defined "/data/idt/features/" directory.

0-4- Extract the content of the "data/groundTruth/" inside our defined "/data/groundTruth/" directory. (already done)

0-5- Extract the content of the "data/transcripts/" inside our defined "/data/transcripts/" directory. (already done)

0-6- Place all the .py files in the same directory as data

Execution

1-0- Go to options.py and change the parameters if desired.

1-1- Type the following command line in terminal:

python main.py

###############################

[1] Y. Abu Farha and J. Gall. MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019

[2] A. Richard, H. Kuehne, A. Iqbal, J. Gall: NeuralNetwork-Viterbi: A Framework for Weakly Supervised Video Learning in IEEE Int. Conf. on Computer Vision and Pattern Recognition, 2018