Per Pixel Prediction

Per-pixel prediction, a core task in computer vision, aims to assign a label or value to each pixel in an image, enabling applications like semantic segmentation and depth estimation. Current research focuses on improving the accuracy and efficiency of these predictions, exploring both convolutional neural networks and transformer-based architectures like mask transformers, which predict labels for groups of pixels rather than individually. Efforts are underway to enhance robustness against adversarial attacks and to leverage semi-supervised learning techniques to reduce the need for large labeled datasets, ultimately driving advancements in areas such as autonomous driving and medical image analysis.