Semantic Segmentation Problem

Semantic segmentation, a core computer vision task, aims to assign a semantic label to each pixel in an image, effectively creating a pixel-wise map of the scene. Current research focuses on improving robustness and accuracy across diverse domains and data types (e.g., RGB, depth, LiDAR), employing advanced architectures like transformers and HRNets, and incorporating techniques such as fuzzy loss functions and contrastive learning to address challenges like class imbalance and noisy data. These advancements are crucial for applications ranging from autonomous driving and remote sensing to medical image analysis and 3D reconstruction, driving progress in scene understanding and object recognition.