Zero Shot Semantic Segmentation

Zero-shot semantic segmentation aims to segment images into meaningful regions without requiring training data for the specific object classes present. Current research focuses on leveraging pre-trained vision-language models like CLIP, and architectures such as transformers, to align textual descriptions with image features for pixel-level classification. Methods often incorporate multi-scale processing, attention mechanisms (e.g., Sinkhorn attention), and strategies to mitigate biases towards seen classes, improving the generalization to unseen objects. This field is significant because it reduces the reliance on extensive labeled datasets, potentially accelerating progress in various applications, including medical image analysis, robotics, and remote sensing.