Moving Window Regression: A Novel Approach to Ordinal Regression

CVPR 2022  ·  Nyeong-Ho Shin, Seon-Ho Lee, Chang-Su Kim ·

A novel ordinal regression algorithm, called moving window regression (MWR), is proposed in this paper. First, we propose the notion of relative rank ($\rho$-rank), which is a new order representation scheme for input and reference instances. Second, we develop global and local relative regressors ($\rho$-regressors) to predict $\rho$-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 $\rho$-rank within the window. Extensive experiments results show that the proposed algorithm achieves the state-of-the-art performances on various benchmark datasets for facial age estimation and historical color image classification. The codes are available at

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Age And Gender Classification Adience Age MWR Accuracy (5-fold) 62.6 # 7
Age Estimation CACD MWR MAE 4.41 # 11
Age Estimation ChaLearn 2015 MWR MAE 2.95 # 3
Age Estimation FGNET MWR MAE 2.23 # 1
Age Estimation MORPH Album2 MWR MAE 2.00 # 4
CS 95.0 # 1
Age Estimation MORPH album2 (Caucasian) MWR MAE 2.13 # 1
Age Estimation UTKFace MWR MAE 4.37 # 5


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