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U-Net and U-Net inspired architectures have been quite popular in the medical image-related tasks ever since it was first introduced. There have been several improved versions of U-Net designed for specific tasks that followed. One such example is Attention U-Net, extremely popular for Pancreas Segmentation. Other examples of architectures that have ...


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tl;dr What does that mean in the context of this paper? With "coarse segmentation" the author means a segmentation that doesn't have much detail. "Fine segmentation", on the other hand, refers to a segmentation with a high level of detail. But also more importantly [what does that mean in the context of] general computer vision? The most common use ...


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This is a really cool problem. You already have a working model here are a few different ways of going forward with the project. Grouping text based on locality. "no segmentation" Text region extraction in a document image based on the Delaunay tessellation Segmentation Multiscale Edge-Based Text Extraction from Complex Images Training a map of the ...


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It depends on what is your ultimate goal. If your goal is to simply classify the object in the image, having more complex output won't help. Simpler output representation yields better result. If your goal is to detect the bounding box, output the bounding box. There is no need for a more complex output feature. If you use a segmentation method for bounding ...


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If we seek proven working source code to plug into a GPLv2-licence compatible solution, we should at least consider autotrace. Its source code is open for review. It can be tested against the example images we have and, if it works fine, called by our GPLv2 software. We can even use the calling code in Inkscape's plug-in image tracing implementation as a ...


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You can find leaderboards as well as code at this address. For now, HRNetV2 leads the game. The U-Net architecture is part of a broad family of network architectures that aggregate multi-scale features to extract finer details useful for semantic segmentation. Examples are Feature Pyramidal Networks (FPN), Hourglass, Encoder-Decoder, MatrixNet, etc...


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If your interest is positional information, encode it! This could include learning an embedding for each position and leveraging that in your model. You could also use an approach to hard-encode rather than learn it (kinda like adding sinusoids in the transformer paper Attention is All You Need an example of a paper that encodes the 2D positional info: ...


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