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In this video, Shrini discusses the evaluation of semantic segmentation using the intersection over union (IoU) approach. Semantic segmentation involves classifying individual pixels within an image rather than categorizing the entire image. For instance, instead of identifying an image as a cat or dog, it identifies which pixels correspond to those animals. Shrini highlights the limitations of using accuracy as a metric, especially in cases of class imbalance common in multi-class problems. He emphasizes that accuracy, derived from comparing predictions to ground truth, may not effectively represent performance in such scenarios.