Most state-of-the-art instance-level human parsing models adopt two-stage anchor-based detectors and, therefore, cannot avoid the heuristic anchor box design and the lack of analysis on a pixel level. To address these two issues, we have designed an instance-level human parsing network which is anchor-free and solvable on a pixel level. It consists of two simple sub-networks: an anchor-free detection head for bounding box predictions and an edge-guided parsing head for human segmentation. The anchor-free detector head inherits the pixel-like merits and effectively avoids the sensitivity of hyper-parameters as proved in object detection applications. By introducing the part-aware boundary clue, the edge-guided parsing head is capable to distinguish adjacent human parts from among each other up to 58 parts in a single human instance, even overlapping instances. Meanwhile, a refinement head integrating box-level score and part-level parsing quality is exploited to improve the quality of the parsing results. Experiments on two multiple human parsing datasets ( i.e. , CIHP and LV-MHP-v2.0) and one video instance-level human parsing dataset ( i.e. , VIP) show that our method achieves the best global-level and instance-level performance over state-of-the-art one-stage top-down alternatives.

中文翻译：

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AIParsing：无锚实例级人类解析


大多数最先进的实例级人体解析模型采用两级基于锚点的检测器，因此无法避免启发式锚框设计和缺乏像​​素级分析。为了解决这两个问题，我们设计了一个实例级的人体解析网络，该网络是无锚的并且可以在像素级上解决。它由两个简单的子网络组成：用于边界框预测的无锚检测头和用于人体分割的边缘引导解析头。无锚检测头继承了类像素的优点，并有效避免了超参数的敏感性，这一点在物体检测应用中得到了证明。通过引入部位感知边界线索，边缘引导解析头能够在单个人体实例中区分相邻的人体部位，最多可达 58 个部位，甚至是重叠的实例。同时，利用集成框级分数和部分级解析质量的细化头来提高解析结果的质量。在两个多个人类解析数据集（即 CIHP 和 LV-MHP-v2.0）和一个视频实例级人类解析数据集（即 VIP）上的实验表明，我们的方法在最先进的单阶段自上而下的替代方案。