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Segmentation architectures in computer vision refer to the design principles and specific network structures used to divide an image into meaningful regions or segments. The primary goal of these architectures is to assign a class label to every pixel in an image, enabling a detailed understanding of the image content. This differs from image classification, which assigns a single label to the entire image, and object detection, which draws bounding boxes around objects.

Early approaches to segmentation often involved post-processing steps applied to classification networks. However, the development of specialized architectures has led to significant improvements in accuracy and efficiency. Fully Convolutional Networks (FCNs) were a foundational development, replacing fully connected layers in traditional convolutional neural networks with convolutional layers. This allowed them to process images of arbitrary size and produce spatial predictions.

Encoder-decoder architectures are a common paradigm. The encoder part, typically a standard convolutional network, progressively downsamples the image, capturing high-level semantic information. The decoder part then upsamples this information, gradually recovering spatial resolution and producing a pixel-wise segmentation map. Skip connections, often used to connect feature maps from the encoder to corresponding layers in the decoder, help to preserve fine-grained details lost during downsampling.

More advanced architectures like U-Net, DeepLab, and Mask R-CNN build upon these fundamental concepts. U-Net, for example, is well-known for its symmetric encoder-decoder structure with extensive skip connections, making it highly effective for medical image segmentation. DeepLab introduces atrous convolutions (dilated convolutions) to enlarge the receptive field without increasing computational cost, allowing for the capture of multi-scale contextual information. Mask R-CNN extends object detection by adding a branch for predicting segmentation masks, enabling instance segmentation, which distinguishes individual instances of the same object class. The choice of segmentation architecture often depends on the specific application, the desired level of detail, and the available computational resources.