Previous human parsing models are limited to parsing humans into pre-defined classes, which is inflexible for applications that need to handle new classes. In this paper, we define a new one-shot human parsing (OSHP) task that requires parsing humans into an open set of classes defined by any test example. During training, only base classes are exposed, which only overlap with part of test-time classes. To address three main challenges in OSHP, i.e., small sizes, testing bias, and similar parts, we devise a novel End-to-end One-shot human Parsing Network (EOP-Net). Firstly, an end-to-end human parsing framework is proposed to mutually share semantic information with different granularities and help recognize the small-size human classes. Then, we devise two collaborative metric learning modules to learn representative prototypes for base classes, which can quickly adapt to unseen classes and mitigate the testing bias. Moreover, we empirically find that robust prototypes empower feature representations with higher transferability to the novel concepts, hence, we propose to adopt momentum-updated dynamic prototypes generated by gradually smoothing the training time prototypes and employ contrastive loss at the prototype level. Experiments on three popular benchmarks tailored for OSHP demonstrate that EOP-Net outperforms representative one-shot segmentation models by large margins, which serves as a strong benchmark for further research on this new task. The source code will be made publicly available.
翻译:人类先前的剖析模型仅限于将人类分为预先定义的类别, 这对于需要处理新类的应用程序来说是没有灵活性的。 在本文中, 我们定义了一个新的单向人类剖析( OSHP) 任务, 需要将人类分解成一个由任何测试示例定义的开放的类别。 在培训过程中, 只有基础班才被暴露, 仅与测试时班的一部分重叠。 为了应对OSHP的三大挑战, 即小尺寸、 测试偏差和类似部分, 我们设计了一个新型的端对端人类剖析网络( EOP- Net ) 。 首先, 提议一个端对端对端的人类剖析( OS) 框架, 需要将人类分解成一个开放的开放的类别。 然后, 我们设计了两个协作性的标准学习模块, 学习基础班的具有代表性的原型模型, 可以快速适应于隐蔽的班级, 并减轻测试偏差。 此外, 我们从实验中发现, 坚固的源原型模型能够进一步向新概念转移特征, 因此, 我们提议在动力- 升级的模型上, 升级的原型模型 将演示模型 升级的原型模型 升级的原型模型 。