Mask2Former

Mask2Former (Masked-attention Mask Transformer) represents a paradigm shift in computer vision, moving away from specialized architectures for different segmentation tasks toward a unified approach. Developed by researchers at Meta AI (FAIR), it leverages a Transformer-based decoder with masked attention to extract localized features, enabling it to excel at semantic, instance, and panoptic segmentation using a single architecture. In the 2026 market landscape, Mask2Former remains a core benchmark and production-grade backbone for high-precision vision tasks. Its architecture consists of three main components: a feature extractor (backbone), a pixel decoder to enhance resolution, and a Transformer decoder that predicts mask embeddings. By focusing attention exclusively on the predicted mask regions, it significantly reduces computational overhead while improving boundary accuracy compared to previous per-pixel classification models. It is widely adopted in industries requiring fine-grained spatial understanding, such as autonomous driving, medical diagnostics, and robotic manipulation, offering a mature ecosystem of pre-trained weights and integration with the Detectron2 library.