Open World Semantic Segmentation

Open-world semantic segmentation aims to enable computer vision systems to segment images into meaningful regions, even when encountering objects unseen during training. Current research focuses on leveraging image-text pairs and contrastive learning to learn robust visual representations and generate accurate segmentation masks for novel classes, often employing clustering techniques to group unknown objects. This capability is crucial for applications like autonomous driving and robotics, where systems must adapt to unpredictable environments and continuously expand their knowledge base without extensive retraining. The field is actively developing methods for both zero-shot and incremental learning scenarios, improving the generalization and robustness of semantic segmentation models.