Text Supervised Semantic Segmentation

Text-supervised semantic segmentation aims to segment images into meaningful regions using only image-text pairs as training data, eliminating the need for expensive pixel-level annotations. Current research focuses on improving the alignment between textual descriptions and visual segments through techniques like contrastive learning, prompt engineering, and the incorporation of large language models (LLMs) to refine class representations and generate more accurate segmentation masks. This approach holds significant promise for advancing open-vocabulary semantic segmentation and enabling applications requiring efficient and scalable image understanding, particularly in medical imaging and other domains with limited labeled data.