Mirror Segmentation via Semantic-aware Contextual Contrasted Feature Learning

Mirrors are everywhere in our daily lives. Existing computer vision systems do not consider mirrors, and hence may get confused by the reflected content inside a mirror, resulting in a severe performance degradation. However, separating the real content outside a mirror from the reflected content inside it is non-trivial. The key challenge is that mirrors typically reflect contents similar to their surroundings, making it very difficult to differentiate the two. In this article, we present a novel method to segment mirrors from a single RGB image. To the best of our knowledge, this is the first work to address the mirror segmentation problem with a computational approach. We make the following contributions: First, we propose a novel network, called MirrorNet+, for mirror segmentation, by modeling both contextual contrasts and semantic associations. Second, we construct the first large-scale mirror segmentation dataset, which consists of 4,018 pairs of images containing mirrors and their corresponding manually annotated mirror masks, covering a variety of daily-life scenes. Third, we conduct extensive experiments to evaluate the proposed method and show that it outperforms the related state-of-the-art detection and segmentation methods. Fourth, we further validate the effectiveness and generalization capability of the proposed semantic awareness contextual contrasted feature learning by applying MirrorNet+ to other vision tasks, i.e., salient object detection and shadow detection. Finally, we provide some applications of mirror segmentation and analyze possible future research directions. Project homepage: https://mhaiyang.github.io/TOMM2022-MirrorNet+/index.html .