Body: <p>I'm building a CNN/3DCNN model that classifies hand gestures. The problem is that the actual gesture occupies only like 1% of the whole image. That means that an enormous amount of convolutional operations is done on the "empty" parts of the image, which is useless. </p>
  
  <p>Is there a way to solve this problem? I was thinking about a MaxPooling layer with a giant pool size, but near features that are extracted from the gesture will be probably "compressed" in only 1 feature.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-03-10T21:46:15.307Z
Comments.Text: I think you can enforce such behavior by modifying the loss function. However, is cropping images out of question in your case? Removing empty parts seem like a simpler approach to me.
Comments.UserDisplayName: SpiderRico
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-03-10T21:55:42.513Z
Comments.Text: Maybe you could use object detection to locate the hand first. Which technique you use really depends on the background. What is the background by the way?
Comments.UserDisplayName: Alexander Soare
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-03-10T22:02:03.157Z
Comments.Text: By the way, "adding focus" is essentially what Faster R-CNN does by incorporating an RPN into the model.
Comments.UserDisplayName: Alexander Soare
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-03-11T10:12:55.567Z
Comments.Text: I was also thinking about object detection first, but that would resize the hand to another resolution, causing images to have different sizes
  EDIT: Nevermind, actually, after the hand gets detected, you can just resize the image to the resolution you want. The only problem is that I need datasets both for gestures and hand classification, wich can be a little tricky
Comments.UserDisplayName: Orly
Tags: <deep-learning><convolutional-neural-networks>
Title: Is there a way to add "focus" on parts of the image when using CNNs?
UserDisplayName: Orly
ThreadText: # Is there a way to add "focus" on parts of the image when using CNNs?
  
  (Asked by: username_0 on 2020-03-09)
  
  I'm building a CNN/3DCNN model that classifies hand gestures. The problem is that the actual gesture occupies only like 1% of the whole image. That means that an enormous amount of convolutional operations is done on the "empty" parts of the image, which is useless.
  
  Is there a way to solve this problem? I was thinking about a MaxPooling layer with a giant pool size, but near features that are extracted from the gesture will be probably "compressed" in only 1 feature.
  
  **Tags:** deep-learning, convolutional-neural-networks
  
  > **Comments:**
  > **username_1**: I think you can enforce such behavior by modifying the loss function. However, is cropping images out of question in your case? Removing empty parts seem like a simpler approach to me.  
  > **username_2**: Maybe you could use object detection to locate the hand first. Which technique you use really depends on the background. What is the background by the way?  
  > **username_2**: By the way, "adding focus" is essentially what Faster R-CNN does by incorporating an RPN into the model.  
  > **username_0**: I was also thinking about object detection first, but that would resize the hand to another resolution, causing images to have different sizes EDIT: Nevermind, actually, after the hand gets detected, you can just resize the image to the resolution you want. The only problem is that I need datasets both for gestures and hand classification, wich can be a little tricky  
  
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  ## 0 answers
  
  