EAGER

Abstract

Image understanding is a fundamental task for many multimedia and computer vision applications, such as self-driving, multimedia retrieval, and augmented reality, etc. In this paper, we demonstrate that edge detection could aid image understanding tasks such as semantic segmentation, optical flow estimation, and object proposal generation. Based on our recent research efforts on edge detection, we develop a robust and efficient Edge-Aided imaGe undERstanding system named as EAGER. EAGER is built on a compact and efficient edge detection module, which is constructed with a bi-directional cascade network, multi-scale feature enhancement, and layer-specific training supervision, respectively. Based on detected edges, EAGER achieves accurate semantic segment, optical flow estimation, as well as object bounding-box proposal generation for user-uploaded images and videos.