Consistent Segmentation

Consistent segmentation aims to produce temporally and spatially coherent segmentations in images and videos, crucial for applications like medical image analysis and autonomous driving. Current research focuses on integrating large language models with segmentation models (like Segment Anything Model) to achieve language-instructed segmentation and tracking, employing techniques such as consistency training and optimal transport to improve temporal coherence across frames. These advancements improve the accuracy and reliability of segmentation, leading to more robust and interpretable results in various fields, particularly where temporal consistency is critical for accurate analysis and decision-making.