Convolutional Neural Networks (CNNs) have revolutionized image processing, but they still have several limitations in computer vision tasks. One major limitation is that CNNs require large amounts of labeled data for training. The lack of sufficient data, especially in specialized fields like medical imaging, can lead to poor generalization and overfitting. Additionally, CNNs struggle with handling spatial relationships in images that may be distorted or have significant variations in scale and orientation. Despite advancements like data augmentation, CNNs can still perform poorly when faced with images that don’t match their training distribution. Another limitation is the computational cost. CNNs can be resource-intensive, especially when dealing with high-resolution images or deep architectures, which require substantial GPU power and memory. This can make them difficult to deploy in real-time applications or on devices with limited resources. Furthermore, CNNs tend to focus more on local features rather than global context. This can be problematic in scenarios where long-range dependencies between objects or areas in the image are important, such as in scene understanding or object recognition over large distances.
What are the limitations of CNN in computer vision?
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