AI&BigData Conference 2017 Oles Petriv "Semantic image segmentation using word embeddings."

1. Semantic image
segmentatation
using word embeddings

2. What is semanic image segmentation ?

3. Types of semantic segmentation problems

4. Getting context from image:

5. Getting context from image:

6. Getting context from image:

7. Our arsenal: Convolution

8. Our arsenal: Convolution

9. How do learned visual features look like ?
low level:

10. How do learned visual features look like ?
higher level:

11. How do learned visual features look like ?

12. Our arsenal: skip connections

13. Simple SegNet:

14. Context problem:

15. Pyramid Pooling:

16. Softmax loss:

17. Softmax loss:

18. Datasets: COCO

19. Datasets: COCO-stuff

20. Datasets: COCO-stuff class tree

21. Datasets: ADE20K

22. Why word embeddings ?

23. Word embeddings: context windows

24. Word embeddings: skip gramm vs CBoW

25. Word embeddings: concept space

26. Word embeddings: cosine distance

27. Word embeddings: cosine distance

28. Word embeddings: cosine distace distribution

29. Word embeddings: language graph

30. map classes to wordnet synsets

31. classical one-hot segmentation labels

32. 3d tensor of word vector labels

33. Loss : per-tube cosine distance

34. Loss : per-tube cosine distance

35. result: vocabulary free semantic segmentation

36. Questions: