How do semantic segmentation methods (UNet), and instance segmentation methods (mask R-CNN) rely on convolutional operations to learn spatial contextual information?
Semantic segmentation and instance segmentation both heavily rely on convolutional operations, specifically convolutional neural networks (CNNs).
In semantic segmentation, the goal is to assign each pixel in an image to a specific class. This requires analyzing the local context around each pixel, which can be achieved using sliding convolutional filters that scan the entire image. By applying convolutional operations to these filters, the network can learn to detect features such as edges, corners, and textures, which are then used to make predictions about the class of each pixel.
Instance segmentation goes a step further than semantic segmentation by not only assigning each pixel to a specific class but also identifying individual instances of that class. This requires the network to differentiate between objects that may overlap or occlude one another. Similar to semantic segmentation, instance segmentation relies on convolutional operations to analyze the local context around each pixel and identify unique object boundaries.
In summary, both semantic segmentation and instance segmentation rely on convolutional operations to extract relevant features from an image and make accurate predictions about the class and/or instance of each pixel.
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