论文标题
部分可观测时空混沌系统的无模型预测
AIParsing: Anchor-free Instance-level Human Parsing
论文作者
论文摘要
大多数最先进的实例级人类解析模型都采用了两阶段的基于锚的探测器，因此无法避免启发式锚盒设计和像素级别缺乏分析。为了解决这两个问题，我们设计了一个实例级人类解析网络，该网络在像素级别上无锚固且可解决。它由两个简单的子网络组成：一个用于边界框预测的无锚检测头和一个用于人体分割的边缘引导的解析头。无锚探测器头继承了像素样的优点，并有效地避免了对象检测应用中证明的超参数的灵敏度。通过引入部分感知的边界线索，边缘引导的解析头能够将相邻的人类部分与彼此区分开，最多可在一个人类实例中，甚至重叠的实例。同时，利用了精炼的头部整合盒子级得分和部分分析质量，以提高解析结果的质量。对两个人类解析数据集（即CIHP和LV-MHP-V2.0）和一个视频实例级人类解析数据集（即VIP）进行了实验，这表明我们的方法实现了与目前最佳的全球级别和实例级别的表现，而不是目前的一阶段一阶段的一步替代品。
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.
