Open Vocabulary Semantic Segmentation

Open-vocabulary semantic segmentation (OVSS) aims to assign semantic labels to image pixels without requiring pre-defined categories, enabling the recognition of objects not seen during training. Current research focuses on adapting vision-language models like CLIP, often in conjunction with other foundation models (e.g., SAM, DINO), to achieve this, employing techniques such as multi-resolution processing, pseudo-mask generation, and contrastive learning to improve accuracy and efficiency. OVSS holds significant promise for advancing various applications, including autonomous driving, remote sensing, and medical image analysis, by enabling more flexible and robust image understanding.