Semantic Segmentation Benchmark

Semantic segmentation benchmarks evaluate the performance of algorithms that assign semantic labels to each pixel in an image, enabling detailed scene understanding. Current research focuses on improving accuracy and efficiency, particularly in challenging scenarios like limited labeled data (semi-supervised and active learning), open-vocabulary settings (leveraging vision-language models), and continual learning for adapting to evolving environments. These advancements are crucial for applications such as autonomous driving, robotics, and remote sensing, where accurate and efficient real-time scene understanding is paramount. Benchmark datasets and evaluation protocols are constantly evolving to better reflect real-world complexities and drive progress in the field.