Data-driven approaches for edge detection have proven effective and achieve top results on modern benchmarks. However, all current data-driven edge detectors require manual supervision for training in the form of hand-labeled region segments or object boundaries. Specifically, human annotators mark semantically meaningful edges which are subsequently used for training. Is this form of strong, high-level supervision actually necessary to learn to accurately detect edges? In this work we present a simple yet effective approach for training edge detectors without human supervision. To this end we utilize motion, and more specifically, the only input to our method is noisy semi-dense matches between frames. We begin with only a rudimentary knowledge of edges (in the form of image gradients), and alternate between improving motion estimation and edge detection in turn. Using a large corpus of video data, we show that edge detectors trained using our unsupervised scheme approach the performance of the same methods trained with full supervision (within 3-5 ). Finally, we show that when using a deep network for the edge detector, our approach provides a novel pre-training scheme for object detection.
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@cite_2: How the visual system comes to bind diverse image regions into whole objects is not well understood. We recently had a unique opportunity to investigate this issue when we met three congenitally blind individuals in India. After providing them treatment, we studied the early stages of their visual skills. We found that prominent figural cues of grouping, such as good continuation and junction structure, were largely ineffective for image parsing. By contrast, motion cues were of profound significance in that they enabled intraobject integration and facilitated the development of object representations that permitted recognition in static images. Following 10 to 18 months of visual experience, the individuals' performance improved, and they were able to use the previously ineffective static figural cues to correctly parse many static scenes. These results suggest that motion information plays a fundamental role in organizing early visual experience and that parsing skills can be acquired even late in life.
Motion plays a key role for grouping and object recognition in the human visual system @cite_42 . In particular, @cite_2 studied the visual skills of individuals recovering from congenital blindness and showed that motion cues were essential to help facilitate the development of object grouping and representation. Our work is inspired by these findings: we aim to learn an edge detector using motion cues.