Abstract: Class agnostic object mask generation uses a vision transformer-based auto-labeling framework requiring only images and object bounding boxes to generate object (segmentation) masks. The generated object masks, images, and object labels may then be used to train instance segmentation models or other neural networks to localize and segment objects with pixel-level accuracy. The generated object masks may supplement or replace conventional human generated annotations. The human generated annotations may be misaligned compared with the object boundaries, resulting in poor quality labeled segmentation masks. In contrast with conventional techniques, the generated object masks are class agnostic and are automatically generated based only on a bounding box image region without relying on either labels or semantic information.

Abstract: Apparatuses, systems, and techniques to train one or more neural networks. In at least one embodiment, one or more neural networks are trained to perform segmentation tasks based at least in part on training data comprising bounding box annotations.