[CVPR 2022] Moving Window Regression: A Novel Approach to Ordinal Regression

Official Pytorch Implementation of the CVPR 2022 paper, "Moving Window Regression: A Novel Approach to Ordinal Regression."

Paper

A novel ordinal regression algorithm, called moving window regression (MWR), is proposed in this paper. First, we propose the notion of relative rank (ρ-rank), which is a new order representation scheme for input and reference instances. Second, we develop global and local relative regressors (ρ-regressors) to predict ρ-ranks within entire and specific rank ranges, respectively. Third, we refine an initial rank estimate iteratively by selecting two reference instances to form a search window and then estimating the ρ-rank within the window.

The full paper can be found via the link above.

Datasets

• MORPH II
• CLAP2015
• CACD
• UTK

Dependencies

• Python 3
• Pytorch

Preprocessing

We use MTCNN for face detection and face alignment code provided from pyimagesearch for face alignment.

Pretrained Models and Reference Lists

You can download pretrained models and reference lists here.

Test

Use the following command for evaluation.

python op.py --dataset Dataset --regression Regression_type --experiment_setting Experimental_setting --im_path Image_path