DeepFix Convolutional Neural Network

A DeepFix Convolutional Neural Network is a Convolutional Neural Network that predicts human eye fixations on images.

• AKA: DeepFix CNN.
• Context:
  • It was initially developed by Kruthiventi et al. (2017).
• Example(s):
  • …
• Counter-Example(s):
  • DeepFix Program Repair System,
  • DeepFix Sequence-to-Sequence Neural Network.
• See: Deep Learning Neural Network, Multi-Layered Neural Network, VGG Net.

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References

2017

• (Kruthiventi et al., 2017) ⇒ Srinivas S. S. Kruthiventi, Kumar Ayush, and R. Venkatesh Babu. (2017). “DeepFix: A Fully Convolutional Neural Network for Predicting Human Eye Fixations.” In: IEEE Transactions on Image Processing Journal, 26(9). doi:10.1109/TIP.2017.2710620
  • QUOTE: In this work, we propose a fully convolutional neural network - DeepFix, for predicting human eye fixations on images in the form of a saliency map. Our model, inspired from VGG net [20], is a very deep network with 20 convolutional layers, each of a small kernel size, operating in succession on an image. The network is designed to capture object-level semantics, which can occur at multiple scales, efficiently through inception style [21] convolution blocks. Each inception module consists of a set of convolution layers with different kernel sizes operating in parallel. The global context of the scene, which is crucial for saliency prediction, is captured using convolutional layers with very large receptive fields. These layers are placed towards the end of the network and replace the densely connected inner product layers commonly present in convolutional net …

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