Fast Proposals for Image and Video Annotation using Modified Echo State Networks

Deep learning frameworks for computer-vision applications require fast and scalable annotation systems. Since manually annotated data for semantic segmentation tasks is time-consuming and tough to quality assure, accurate and automated region-based proposals can significantly aid high quality data annotation. In this work, we propose modified Echo State Network (ESN) models that iteratively learn from a small subset of data (20-30% images) and adapt to a variety of semantic segmentation goals without manual supervision on test images. We observe that the modified ESN model that relies on 3 x 3 pixel neighborhood features scales across segmentation tasks with mean segmentation F_scores in the range of 0.58-0.87 for complete foreground and specific foreground segmentation tasks, respectively. Thus, the proposed methods can be specifically useful for fast semantic proposal estimation to enhance the annotation resourcefulness for time sensitive applications in the automotive field.