  In this work, we introduce Mask-JEPA, a self-supervised learning framework
tailored for mask classification architectures (MCA), to overcome the
traditional constraints associated with training segmentation models. Mask-JEPA
combines a Joint Embedding Predictive Architecture with MCA to adeptly capture
intricate semantics and precise object boundaries. Our approach addresses two
critical challenges in self-supervised learning: 1) extracting comprehensive
representations for universal image segmentation from a pixel decoder, and 2)
effectively training the transformer decoder. The use of the transformer
decoder as a predictor within the JEPA framework allows proficient training in
universal image segmentation tasks. Through rigorous evaluations on datasets
such as ADE20K, Cityscapes and COCO, Mask-JEPA demonstrates not only
competitive results but also exceptional adaptability and robustness across
various training scenarios. The architecture-agnostic nature of Mask-JEPA
further underscores its versatility, allowing seamless adaptation to various
mask classification family.
