Agnostic Mask

Agnostic masks, class-agnostic segmentations that identify regions of interest without specifying object categories, are increasingly used in computer vision to improve the generalization and efficiency of various tasks. Current research focuses on leveraging these masks within diverse applications, including scene change detection, 3D object discovery from neural radiance fields, and image compression, often integrating them with pre-trained models like Segment Anything Model (SAM) or large language models (LLMs). This approach enhances model robustness to unseen data and reduces reliance on extensive labeled datasets, leading to more efficient and generalizable algorithms across multiple computer vision domains.