Ke Chen 1 , Shaogang Gong 1 , Tao Xiang 1 , Chen Change Loy 2

1. VGG reading group presentation by Minh Hoai Ke Chen1, ShaogangGong1, Tao Xiang1, Chen Change Loy2 1. Queen Mary, University of London 2. The Chinese University of Hong Kong Cumulative Attribute Space for Age and Crowd Density Estimation

2. Tasks How old are they? How many people? What is the head angle?

3. A Regression Framework Input images Features Label space AAM feature Learn the mapping Feature extraction Label (age, count) Segment feature Regression Edge feature Texture feature

4. Challenge – Sparse and Unbalanced data Data distribution of FG-NET Dataset

5. Challenge – Sparse and Unbalanced data Data distribution of UCSD Dataset

6. Proposed Approach • Solution: • Attribute Learning can address data sparsity problem -- • Exploits the shared characteristics between classes • Has sematic meaning • Question to address: • How to exploit cumulative dependent nature of labels in regression? …… …… …… Age 20 Age 21 Age 60

7. Cumulative Attribute Non-cumulative attribute (independent) Cumulative attribute (dependent) 0 1 … 1 20 … Age 20 0 Vs. 1 1 20th 0 0 0 0 the rest … … 0 0

8. Limitation of Non-cumulative Attribute 0 0 0 … … … 0 0 0 0 0 1 20th … 1 0 21st Age 20 0 … Age 21 Age 60 0 1 0 60th 0 0 0 … … … 0 0 0

9. Advantages of Cumulative Attribute 1 1 1 1 1 1 20 … … … 21 40 attributes change 1 attribute changes 1 60 1 1 1 0 1 Age 60 Age 21 Age 20 … … 0 0 1 the rest 0 0 0 … … … 0 0 0

10. Proposed Framework The task xi yi ai Feature vector(e.g., intensity) Label (e.g., age) Cumulative attribute 1 1 Regressor Regressor 2 1 yi 1 … … 0 0

11. Proposed Framework Our task How are these regressors learned? xi yi ai Feature vector(e.g., intensity) Label (e.g., age) Cumulative attribute 1 1 Regressor Regressor 2 1 See next slide! Can use any regression method: Support Vector Regression, Ridge Regression yi 1 … … 0 0

12. Regressor for Cumulative Attributes Image feature vector Cumulative attribute Parameters to learn # of training data Regularization Regression error Closed-form solution:

13. Experiments

14. Baseline Methods and Name Abbreviation CA-SVR 1 2 yi … … 1 1 0 0 1 xi SVR yi Cumulative attributes Feature vector Label Support Vector Regression (SVR) SVR Non-Cumulative attributes NCA-SVR 1 2 yi … … 1 0 0 0 0

15. Cumulative (CA) vs. Non-cumulative (NCA) Mean absolute error (lower is better) Percentage of prediction within 5 years (higher is better) Age Estimation

16. Cumulative (CA) vs. Non-cumulative (NCA) Mean absolute error (lower is better) Mean squared error (lower is better) Mean deviation error (lower is better) Crowd Counting

17. Crowd Counting Results Based on regression Ridge Regression without attributes Proposed method, RR: Ridge Regression CA-RR: our method; LSSVR: Suykens et al, IJCNN, 2001; KRR: An et al, CVPR, 2007; RFR: Liaw et al, R News, 2002; GPR: Chan et al, CVPR, 2008; RR: Chen et al, BMVC, 2012;

18. Age Estimation Results Not based on regression What is OHRank? Proposed method, SVR: Support Vector Regression CA-SVR: our method; AGES: Geng et al, TPAMI, 2007; RUN: Yan et al, ICCV, 2007; Ranking: Yan et al, ICME, 2007; RED-SVM: Chang et al, ICPR, 2010; LARR: Guo et al, TIP, 2008; MTWGP: Zhang et al, CVPR, 2010; OHRank: Chang et al, CVPR, 2011; SVR: Guo et al, TIP, 2008;

19. OHRank - Ordinal HyperplanesRanker Delta 0/1 function SVM score for older than k This is 104 slower than closed-form solution of regression

20. Robustness Against Sparse and Unbalanced Data (Effects of removing random/certain label groups) Age Estimation Crowd Counting

21. Feature Selection by Attributes Shape plays a more important role than texture for youngerages.

22. Summary • Has a simple and neat idea • Exploits cumulative dependent nature of label space • Addresses sparse and unbalanced data problem

23. Support Vector Regression

24. Datasets