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Hierarchical Subquery Evaluation for Active Learning on a Graph. Oisin Mac Aodha, Neill Campbell, Jan Kautz, Gabriel Brostow. CVPR 2014. University College ...
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1.Hierarchical Subquery Evaluation for Active Learning on a Graph Oisin Mac Aodha , Neill Campbell, Jan Kautz , Gabriel Brostow CVPR 2014 University College London 1

2.Cat Dog Horse 2 Large Image Collections https:// www.flickr.com/photos/cmichel67

3.Large Image Collections https:// www.flickr.com/photos/cmichel67 Cat Dog Horse Labeling large image collections is tedious 3

4.Acquiring Annotations 4 https:// www.flickr.com/photos/usnavy https:// www.flickr.com/photos/rdecom Crowdsourcing Specialized Knowledge Expert time is valuable!

5.5 Active Learning Oracle AL Algorithm User Query Label Unlabeled Dataset

6.Number of user queries Test Accuracy 1 0 6 Learning Curves

7.Number of user queries 1 0 7 Learning Curves Test Accuracy

8.Number of user queries 1 0 8 Learning Curves Test Accuracy

9.Number of user queries 1 0 9 Learning Curves Test Accuracy

10.Learning Curves Number of user queries 1 0 10 Test Accuracy

11.Learning Curves Number of user queries 1 0 We want the largest area under the learning curve 11 Test Accuracy

12.Learning Curves 1 0 12 Test Accuracy The number of unlabeled images can be very large!

13.13 Active Learning Wish List

14.Fast updating of classifier for interactive labeling 14 Active Learning Wish List

15.Fast updating of classifier for interactive labeling Exploit structure in unlabeled data 15 Active Learning Wish List

16.Fast updating of classifier for interactive labeling Exploit structure in unlabeled data Consistent performance across different datasets 16 Active Learning Wish List

17.Fast updating of classifier for interactive labeling Exploit structure in unlabeled data Consistent performance across different datasets Make the most of the expert’s time 17 Active Learning Wish List Graph Based Semi-Supervised Learning Perplexity Graph Construction Our Hierarchical Subquery Evaluation

18.18 Related Work Video Segmentation Fathi et al. BMVC 2011 Action Detection Bandla and Grauman ICCV 2013 Gaussian Random Fields Zhu et al. ICML 2003 Semantic Segmentation Vezhnevets et al. CVPR 2012 RALF: Reinforced Active Learning Ebert et al. CVPR 2012 … Image Classification Kapoor et al. ICCV 2007 …

19. x i φ ( ) = 19 Supervised Classification

20. x j φ ( ) = 20 Supervised Classification

21.21 Supervised Classification

22.22 Supervised Classification Decision Boundary

23.Semi-supervised learning using G aussian fields and harmonic functions X . Zhu, Z. Ghahramani , J . Lafferty ICML 2003 F i = P(f(x i ) == class1 ) 23 w ij Semi-Supervised Learning

24.Semi-Supervised Learning 24 F i = P(f(x i ) == class1 ) w ij

25.Graph Construction 25 Stochastic neighbor embedding G. Hinton and S. Roweis NIPS 2002

26.26 Graph Active Learning

27.Example 2 Class Graph 27

28.Example 2 Class Graph 28 Ground Truth

29.Example 2 Class Graph 29 Active Learning Strategies