Generalization to Video Capsules - From Convolutional to Video Capsule Networks

Generalization to Video Capsules - From Convolutional to Video Capsule Networks

UCF CRCV via YouTube Direct link

Quantitative Results -Speed Analysis

32 of 37

32 of 37

Quantitative Results -Speed Analysis

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Generalization to Video Capsules - From Convolutional to Video Capsule Networks

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  1. 1 Intro
  2. 2 Computational Cost of Capsule Voting
  3. 3 Conventional Convolutional Layers
  4. 4 Convolutional Capsule Layers
  5. 5 Capsule Pooling
  6. 6 Video Capsule Networks
  7. 7 Video Action Detection Networks
  8. 8 VideoCapsuleNet Architecture
  9. 9 Coordinate Addition
  10. 10 Capsule Masking
  11. 11 VideoCapsuleNet Training
  12. 12 Action Localization Accuracy
  13. 13 Qualitative Results - Entire Videos
  14. 14 Synthetic Dataset Experiments
  15. 15 Summary
  16. 16 Capsules in multiple modalities
  17. 17 Combining Video and Text
  18. 18 Overall Approach
  19. 19 Multi-modal Capsule Routing Algorithm
  20. 20 Full Architecture
  21. 21 Sentence Encoder
  22. 22 Merging Modalities and Masking
  23. 23 Upsampling Network
  24. 24 Quantitative Results - A2D Dataset
  25. 25 Semi-Supervised Video Object Segmentation
  26. 26 VOS using Capsules
  27. 27 Attention Routing
  28. 28 Video Encoder
  29. 29 Frame Encoder with Memory Module
  30. 30 Conv Capsule Layer and Decoder Network
  31. 31 Objective Function
  32. 32 Quantitative Results -Speed Analysis
  33. 33 Qualitative Results - Single Object
  34. 34 Qualitative Results - Multiple Objects
  35. 35 Effect of Memory Module
  36. 36 Effect of the Zooming Module
  37. 37 Effect of Zooming Module

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