深入暗区域以实现鲁棒的阴影检测
Delving into Dark Regions for Robust Shadow Detection
AI总结：
针对现有阴影检测方法在暗区域错误率高的问题，本文提出先学习全局上下文再聚焦暗区域的检测方法，通过DRR和DASA模块提升暗区域判别能力，并在三个数据集上取得最优性能。
摘要：
阴影检测是一项具有挑战性的任务，因为它需要全面理解阴影特性以及全局/局部光照条件。我们在实验中观察到，最先进的深度方法在区分暗区域（即低强度值区域）中的阴影像素与非阴影像素时往往具有更高的错误率。我们对这一问题的关键见解是，现有方法通常从整幅图像全局学习判别性阴影特征，覆盖完整的强度值范围，可能无法学习到暗区域中阴影与非阴影像素之间的细微差异。因此，如果我们能够设计一个模型来聚焦于更窄范围的低强度区域，它可能能够学习到更好的判别性特征用于阴影检测。受此见解启发，我们提出了一种新颖的阴影检测方法，该方法首先在整个图像上学习全局上下文线索，然后放大到暗区域以学习局部阴影表示。为此，我们构建了一个有效的暗区域推荐（DRR）模块来推荐低强度值区域，以及一个新颖的暗感知阴影分析（DASA）模块来从推荐的暗区域中学习暗感知阴影特征。大量实验表明，所提出的方法在三个流行的阴影检测数据集上优于最先进的方法。代码可在 https://github.com/guanhuankang/ShadowDetection2021.git 获取。
英文摘要：
Shadow detection is a challenging task as it requires a comprehensive understanding of shadow characteristics and global/local illumination conditions. We observe from our experiment that state-of-the-art deep methods tend to have higher error rates in differentiating shadow pixels from non-shadow pixels in dark regions (ie, regions with low-intensity values). Our key insight to this problem is that existing methods typically learn discriminative shadow features from the whole image globally, covering the full range of intensity values, and may not learn the subtle differences between shadow and non-shadow pixels in dark regions. Hence, if we can design a model to focus on a narrower range of low-intensity regions, it may be able to learn better discriminative features for shadow detection. Inspired by this insight, we propose a novel shadow detection approach that first learns global contextual cues over the entire image and then zooms into the dark regions to learn local shadow representations. To this end, we formulate an effective dark-region recommendation (DRR) module to recommend regions of low-intensity values, and a novel dark-aware shadow analysis (DASA) module to learn dark-aware shadow features from the recommended dark regions. Extensive experiments show that the proposed method outperforms the state-of-the-art methods on three popular shadow detection datasets. Code is available at https://github.com/guanhuankang/ShadowDetection2021.git.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
