CVPR 2013 Diversity Tutorial Closing Remarks: What can we do with multiple diverse solutions? Dhruv Batra Virginia Tech.

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Presentation transcript:

CVPR 2013 Diversity Tutorial Closing Remarks: What can we do with multiple diverse solutions? Dhruv Batra Virginia Tech

CVPR 2013 Diversity Tutorial (C) Dhruv Batra2 Example Result Now what?

CVPR 2013 Diversity Tutorial Your Options Nothing –User in the loop (Approximate) Min Bayes Risk –Use solutions to estimate the distribution and optimize Bayes Risk Re-ranking –Pick a good solution from the list (C) Dhruv Batra3 Increasing Side Information

CVPR 2013 Diversity Tutorial Interactive Segmentation Setup –Model: Color/Texture + Potts Grid CRF –Inference: Graph-cuts –Dataset: 50 train/val/test images (C) Dhruv Batra4 Image + ScribblesDiverse 2 nd Best2 nd Best MAPMAP 1-2 Nodes Flipped Nodes Flipped

CVPR 2013 Diversity Tutorial Interactive Segmentation (C) Dhruv Batra % % % (Oracle) M=6 Segmentation Accuracy Better

CVPR 2013 Diversity Tutorial Your Options Nothing –User in the loop (Approximate) Min Bayes Risk –Use solutions to estimate the distribution and optimize Bayes Risk Re-ranking –Pick a good solution from the list (C) Dhruv Batra6

CVPR 2013 Diversity Tutorial Statistics 101 Loss –PCP, Pascal Loss, etc “True” Distribution Expected Loss: Min Bayes Risk (C) Dhruv Batra7

CVPR 2013 Diversity Tutorial Structured Output Problems Min Bayes Risk Two Problems Approximate MBR: (C) Dhruv Batra8 Intractable

CVPR 2013 Diversity Tutorial Semantic Segmentation Setup –Models: Hierarchical CRF [Ladicky et al. ECCV ’10, ICCV ‘09] Second-Order Pooling [Carreira ECCV ‘12] –Inference: Alpha-expansion Greedy –Dataset: Pascal Segmentation Challenge (VOC 2012) 20 categories + background; ~1500 train/val/test images (C) Dhruv Batra9

CVPR 2013 Diversity Tutorial (C) Dhruv Batra10 Large-Margin Re-ranking

CVPR 2013 Diversity Tutorial Semantic Segmentation (C) Dhruv Batra11 InputMAPBest of 10-Div

CVPR 2013 Diversity Tutorial Semantic Segmentation (C) Dhruv Batra12 PACAL Accuracy Better #Solutions / Image MAP [State-of-art circa 2012] 15%-gain possible Same Features Same Model 15%-gain possible Same Features Same Model DivMBest (Oracle) Rand (Re-rank) MBR

CVPR 2013 Diversity Tutorial Your Options Nothing –User in the loop (Approximate) Min Bayes Risk –Use solutions to estimate the distribution and optimize Bayes Risk Re-ranking –Pick a good solution from the list (C) Dhruv Batra13

CVPR 2013 Diversity Tutorial (C) Dhruv Batra14 Large-Margin Re-ranking

CVPR 2013 Diversity Tutorial (C) Dhruv Batra15 Large-Margin Re-ranking

CVPR 2013 Diversity Tutorial (C) Dhruv Batra16 Large-Margin Re-ranking

CVPR 2013 Diversity Tutorial (C) Dhruv Batra17 Large-Margin Re-ranking Discriminative Re-ranking of Diverse Segmentation [Yadollahpour et al., CVPR13, Wednesday Poster]

CVPR 2013 Diversity Tutorial Semantic Segmentation (C) Dhruv Batra18 PACAL Accuracy Better #Solutions / Image MAP [State-of-art circa 2012] DivMBest (Oracle) Rand (Re-rank) DivMBest (Re-ranked) [Y.B.S., CVPR ‘13] MBR

CVPR 2013 Diversity Tutorial Qualitative Results: Success (C) Dhruv Batra19

CVPR 2013 Diversity Tutorial Qualitative Results: Success (C) Dhruv Batra20

CVPR 2013 Diversity Tutorial Qualitative Results: Success (C) Dhruv Batra21

CVPR 2013 Diversity Tutorial Qualitative Results: Failures (C) Dhruv Batra22

CVPR 2013 Diversity Tutorial Qualitative Results: Failures (C) Dhruv Batra23

CVPR 2013 Diversity Tutorial Qualitative Results: Failures (C) Dhruv Batra24

CVPR 2013 Diversity Tutorial Summary All models are wrong Some beliefs are useful Diverse Multiple Solutions –A way to get useful beliefs out. DivMBest + Reranking –Big impact possible on many applications! (C) Dhruv Batra25

CVPR 2013 Diversity Tutorial Summary What does my model believe? (C) Dhruv Batra26 Posterior Summary

CVPR 2013 Diversity Tutorial Thanks! (C) Dhruv Batra27