1 / 27

Closing Remarks: What can we do with multiple diverse solutions?

Closing Remarks: What can we do with multiple diverse solutions?. Dhruv Batra Virginia Tech. Example Result. Now what?. Your Options. Nothing User in the loop (Approximate) Min Bayes Risk Use solutions to estimate the distribution and optimize Bayes Risk Re-ranking

pavel
Download Presentation

Closing Remarks: What can we do with multiple diverse solutions?

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Closing Remarks:What can we do with multiple diverse solutions? Dhruv Batra Virginia Tech

  2. Example Result Now what? (C) Dhruv Batra

  3. Your Options • Nothing • User intheloop • (Approximate) Min Bayes Risk • Use solutions to estimate the distribution and optimize Bayes Risk • Re-ranking • Pick a good solution from the list Increasing Side Information (C) Dhruv Batra

  4. Interactive Segmentation • Setup • Model: Color/Texture + Potts Grid CRF • Inference: Graph-cuts • Dataset: 50 train/val/test images Image + Scribbles MAP 2nd Best MAP Diverse 2nd Best 1-2 Nodes Flipped 100-500 Nodes Flipped (C) Dhruv Batra

  5. Interactive Segmentation Better +3.62% +1.61% Segmentation Accuracy +0.05% (Oracle) (Oracle) (Oracle) M=6 (C) Dhruv Batra

  6. Your Options • Nothing • User intheloop • (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 Batra

  7. Statistics 101 • Loss • PCP, Pascal Loss, etc • “True” Distribution • Expected Loss: • Min Bayes Risk (C) Dhruv Batra

  8. Structured Output Problems • Min Bayes Risk • Two Problems • Approximate MBR: Intractable Intractable (C) Dhruv Batra

  9. 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 Batra

  10. Large-Margin Re-ranking (C) Dhruv Batra

  11. Semantic Segmentation Input MAP Best of 10-Div (C) Dhruv Batra

  12. Semantic Segmentation DivMBest (Oracle) Better 15%-gain possible Same FeaturesSame Model PACAL Accuracy MAP [State-of-art circa 2012] MBR Rand (Re-rank) #Solutions / Image (C) Dhruv Batra

  13. Your Options • Nothing • User intheloop • (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 Batra

  14. Large-Margin Re-ranking (C) Dhruv Batra

  15. Large-Margin Re-ranking (C) Dhruv Batra

  16. Large-Margin Re-ranking (C) Dhruv Batra

  17. Large-Margin Re-ranking Discriminative Re-ranking of Diverse Segmentation [Yadollahpour et al., CVPR13, Wednesday Poster] (C) Dhruv Batra

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

  19. Qualitative Results: Success (C) Dhruv Batra

  20. Qualitative Results: Success (C) Dhruv Batra

  21. Qualitative Results: Success (C) Dhruv Batra

  22. Qualitative Results: Failures (C) Dhruv Batra

  23. Qualitative Results: Failures (C) Dhruv Batra

  24. Qualitative Results: Failures (C) Dhruv Batra

  25. 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 Batra

  26. Summary • What does my model believe? Posterior Summary (C) Dhruv Batra

  27. Thanks! (C) Dhruv Batra

More Related