What's It Going to Cost You?: Predicting Effort vs. Informativeness for Multi-Label Image Annotations

Abstract

Active learning strategies can be useful when manual labeling effort is scarce, as they select the most informative examples to be annotated first. However, for visual category learning, the active selection problem is particularly complex: a single image will typically contain multiple object labels, and an annotator could provide multiple types of annotation (e.g., class labels, bounding boxes, segmentations), any of which would incur a variable amount of manual effort. We present an active learning framework that predicts the tradeoff between the effort and information gain associated with a candidate image annotation, thereby ranking unlabeled and partially labeled images according to their expected "net worth" to an object recognition system. We develop a multi-label multiple-instance approach that accommodates multi-object images and a mixture of strong and weak labels. Since the annotation cost can vary depending on an image's complexity, we show how to improve the active selection by directly predicting the time required to segment an unlabeled image. Given a small initial pool of labeled data, the proposed method actively improves the category models with minimal manual intervention.