Video Semantic Segmentation

Video semantic segmentation aims to automatically assign semantic labels (e.g., car, road, person) to every pixel in a video, requiring robust handling of both spatial and temporal information. Current research emphasizes efficient algorithms that leverage contextual relationships between objects and frames, often employing transformer-based architectures, conditional random fields, or mask propagation techniques to improve accuracy and reduce computational cost. These advancements are crucial for applications such as autonomous driving, video editing, and augmented reality, where accurate and real-time understanding of video content is essential.