Abstract: Various disclosed embodiments are directed to learning-free video object segmentation (VOS) of one or more video frames. In various instances, such VOS does not rely on human supervision or labeling and can be generalized or applied to any video “in the wild.” This is because training and fine-tuning are not required for VOS. Particular embodiments perform VOS based on feature similarity of different sections of a video frame and/or estimated motion similarity of different sections of the video frame, such as via a graph cut on a graph.

Abstract: A corrective noise system receives an electronic version of a fillable form generated by a segmentation network and receives a correction to a segmentation error in the electronic version of the fillable form. The corrective noise system is trained to generate noise that represents the correction and superimpose the noise on the fillable form. The corrective noise system is further trained to identify regions in a corpus of forms that are semantically similar to a region that was subject to the correction. The generated noise is propagated to the semantically similar regions in the corpus of forms and the noisy corpus of forms is provided as input to the segmentation network. The noise causes the segmentation network to accurately identify fillable regions in the corpus of forms and output a segmented version of the corpus of forms having improved fidelity without retraining or otherwise modifying the segmentation network.