Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model (UPC Reading Group 2017)

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Slides by Junting Pan at the UPC Computer Vision Reading Group about the paper: M Cornia, L Baraldi, G Serra, R Cucchiara. Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model. arXiv preprint arXiv:1611.09571

Daten & Analysen

Predicting Human Eye Fixations via an LSTM-based
Saliency Attentive Model
Junting Pan
M Cornia, L Baraldi, G Serra, R Cucchiara. Predicting Human Eye Fixations via an LSTM-based Saliency
Attentive Model. arXiv preprint arXiv:1611.09571

Outline
1. Introduction
2. Model Architecture
3. Experimental Evaluation
4. Implementation Details
5. Results
6. Conclusion

Outline
1. Introduction
2. Model Architecture
a. Dilated Residual Convolutional Networks
b. Attentive Convolutional LSTM
c. Learned Priors
3. Experimental Evaluation
4. Implementation Details
5. Results
6. Conclusion

2. Model Architecture - DRCN
conv4 use dilated rate = 1
conv5 use dilated rate = 3

2. Model Architecture - Attentive ConvLSTM

2. Model Architecture - DRCN
Filter size of 5x5 with dilated rate =3,
which means a receptive field of
17x17

Outline
1. Introduction
2. Model Architecture
3. Experimental Evaluation
4. Implementation Details
5. Results
6. Conclusion
- SALICON 5.000 testing images
- MIT1003 1003 images
- MIT300 300 testing images
- CAT2000 2000 testing images

4. Implementation Details
- Batch size : 10
- RMSprop optimizer
- Residual Network are initialized using ResNet-50 pre trained on ImageNet
- Loss Function : KL-Divergence
- Learning rate : 10e-4, decay by a factor of 10 every two epochs

5. Results - Model Ablation Analysis
DRCN +Conv LSTM +Priors Ground Truth

6. Conclusions
- New Saliency Attentive Model for fixation prediction.
- Attentive Convolutional LSTM that is specifically designed to sequentially
focus on the most salient regions of input images.
- Residual Architecture with dilated filters that maintains the spatial resolution.

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