Language Driven Semantic Segmentation

Language-driven semantic segmentation aims to segment images based on textual descriptions, enabling flexible and adaptable image analysis without relying on extensive pixel-level annotations. Current research focuses on improving the alignment between visual and textual representations, often employing transformer-based architectures and contrastive learning methods within vision-language models like CLIP, to achieve robust zero-shot and few-shot segmentation capabilities. This approach holds significant promise for various applications, including medical image analysis (e.g., identifying infected lung areas in X-rays) and robotics, by enabling more efficient and generalizable image understanding. The development of weakly-supervised and open-vocabulary methods further enhances the practicality and scalability of this technology.