based and each attention mechanism focuses on a specific color model. We help with deep learning to focus on different color models so that each one captures the information of interest in a better way, more efficiently. So we have a pipeline of mobile net because we know that mobile nets are more efficient for mobile architecture.“ In terms of like technical motivation before going into deeper learning, the YCbCr separates the color from the luminance. In this case, we can capture better differences in texture that in RGB were not captured. So they are complementing, it's like a fusion. When we talk about an algorithm that must be used and embedded in a mobile, we want to consider all the variations that are related to the mobile capture, and this is challenging on its own. “For the principle that I mentioned,” Emanuela explains, “we are retraining the network on the converted images in different color spaces. In the WACV paper, we do not retrain. The models are still finetuned in the architecture that I mentioned. In the step ahead, the work that we are doing right now is just to retrain the network from scratch. We transform ImageNet and then we retrain from scratch.” The novelty is also the window of attention mechanism that is focusing on specific color spaces for this ring. When we integrate different sources of information, we want to make sure that they are the diversity, so there is no redundancy. And we know how successful the attention mechanism has been in computer vision. “We are very grateful to all the inventors in computer vision,” exclaims Emanuela “because we are using all the great work they have done!” 19 DAILY WACV Sunday ColFigPhotoAttnNet
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