Urban Scene Segmentation

Urban scene segmentation aims to automatically classify each pixel in an image of a city environment into predefined categories (e.g., road, building, vehicle). Current research focuses on improving the accuracy and robustness of segmentation models, particularly addressing challenges like domain generalization (transferring models trained on simulated data to real-world scenarios) and handling highly imbalanced datasets with fine-grained objects. This involves exploring various deep learning architectures, including convolutional neural networks (CNNs) and transformers, often enhanced with techniques like multi-resolution feature perturbation, contrastive learning, and adversarial training to improve generalization and performance on challenging datasets. Advances in this field are crucial for applications such as autonomous driving, urban planning, and robotics, enabling more accurate and reliable perception of complex urban environments.