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DeepFix: a fully convolutional neural network for predicting human fixations (UPC Reading Group)

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1. DeepFix: A Fully Convolutional Neural Network for Predicting Human Fixations Srinivas S S Kruthiventi, Kumar Ayush, and R. Venkatesh Babu (arXiv October 2015) [URL]…
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  • 1. DeepFix: A Fully Convolutional Neural Network for Predicting Human Fixations Srinivas S S Kruthiventi, Kumar Ayush, and R. Venkatesh Babu (arXiv October 2015) [URL] Slides by Xavier Giró-i-Nieto, from the Computer Vision Reading Group. (27/10/2015) https://imatge.upc.edu/web/teaching/computer-vision-reading-group
  • 2. Introduction 2
  • 3. Introduction 3 Bottom-up attention Automatic Reflexive Stimulus-driven
  • 4. Introduction 4 Top-down attention Subjective’s prior knowledge Expectations Task oriented Memory Behavioral goals
  • 5. Introduction 5 Visual Attentional Mechanisms Bottom-up Automatic Reflexive Stimulus-driven Top-down Subjective’s prior knowledge Expectations Task oriented Memory Behavioral goals
  • 6. Introduction
  • 7. Introduction 7 DeepFixClassic method
  • 8. Introduction 8 mit300 benchmark [URL]
  • 9. Introduction 9 cat200 benchmark [URL]
  • 10. The ingredients 10
  • 11. Very deep network 11 Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014) ● Inspired by Oxford’s VGG net (19 layers). ● 20 layers ● Small kernel sizes.
  • 12. Fully convolutional network (FCN) 12 ● Fully connected layers at the end are replaced by convolutional layers with very large receptive fields. ● They capture the global context of the scene. ● End-to-end training Long, J., Shelhamer, E., & Darrell, T. (2015). Fully Convolutional Networks for Semantic Segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 3431-3440)
  • 13. 13 Inception layers Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. (2015). Going Deeper With Convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1-9) ● GoogLeNet ● Different kernel sizes operating in parallel.
  • 14. 14 Location Biased Convolutional (LBC) layer ● Centre-bias ●
  • 15. The network 15
  • 16. Architecture 16 Small convolutional filters of 3x3 with stride of 1 to allow a large depth without increasing the memory requirement
  • 17. Architecture 17 Max pooling layers (in red) reduce computation.
  • 18. Architecture 18 Gradual increase in the amount of channels to progressively learn richer semantic representations: 64, 128, 256, 512...
  • 19. Architecture 19 Weights initialized from VGG-16 net for stable and effective learning
  • 20. Architecture 20 Convolution kernel 3x3 with hole size 2 have a receptive field of 5x5.
  • 21. Architecture 21 Capture multi-scale semantic structure using two inception style convolutional modules
  • 22. Architecture 22 Very large receptive fields of 25x25 by introducing holes of size 6 in kernels
  • 23. Architecture 23 Location Biased Convolutional (LBC) layers
  • 24. Architecture 24 Location Biased Convolutional (LBC) layers
  • 25. Architecture 25 constant during training learnt during training weights from c’th filter in a convolutional layer input blob
  • 26. Architecture 26 Final output W/8xH/8 is upsampled.
  • 27. Experiments 27
  • 28. Training 28 2nd stage MIT 1003 CAT2000 Mouse clicks from Microsoft CoCo Not mentioned how to go from eye fixations to heat mapa !!
  • 29. Training 29 ● End to end (as JuntingNet) ● Caffeframework ● 1 day in K40 GOU!
  • 30. Results 30
  • 31. Results 31
  • 32. Results 32
  • 33. Results 33
  • 34. Results 34
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