Convolutional Neural Networks: Semantic Segmentation

This lecture covers the use of Convolutional Neural Networks (CNNs) for semantic segmentation, focusing on the transition from patch classification to pixel classification. The instructor explains the limitations of early models and introduces new models designed specifically for pixel classification. Different approaches to semantic segmentation, such as encoder-decoder models and hypercolumns, are discussed. The lecture also delves into the concept of learned decoding using techniques like U-Net and transposed convolutions. The importance of skip connections in maintaining high-resolution information during decoding is highlighted. The session concludes with a summary emphasizing the significance of CNNs in research and industry, encouraging further exploration through reading clubs and additional courses.