Abstract
Despite the noticeable progress in perceptual tasks like detection, instance segmentation and human parsing, computers still perform unsatisfactorily on visually understanding humans in crowded scenes, such as group behavior analysis, person re-identification and autonomous driving, etc. To this end, models need to comprehensively perceive the semantic information and the differences between instances in a multi-human image, which is recently defined as the multi-human parsing task. In this paper, we present a new large-scale database "Multi-Human Parsing (MHP)" for algorithm development and evaluation, and advances the state-of-the-art in understanding humans in crowded scenes. MHP contains 25,403 elaborately annotated images with 58 fine-grained semantic category labels, involving 2-26 persons per image and captured in real-world scenes from various viewpoints, poses, occlusion, interactions and background. We further propose a novel deep Nested Adversarial Network (NAN) model for multi-human parsing. NAN consists of three Generative Adversarial Network (GAN)-like sub-nets, respectively performing semantic saliency prediction, instance-agnostic parsing and instance-aware clustering. These sub-nets form a nested structure and are carefully designed to learn jointly in an end-to-end way. NAN consistently outperforms existing state-of-the-art solutions on our MHP and several other datasets, and serves as a strong baseline to drive the future research for multi-human parsing.
Abstract (translated)
尽管在诸如检测，实例分割和人工解析等感知任务方面取得了显着进步，但计算机在拥挤的场景中仍然无法令人满意地理解人类，例如群体行为分析，人员重新识别和自动驾驶等。为此，模型需要全面地感知多人图像中的语义信息和实例之间的差异，其最近被定义为多人解析任务。在本文中，我们提出了一个新的大型数据库“多人解析（MHP）”，用于算法开发和评估，并推进了在拥挤场景中理解人类的最新技术。 MHP包含25,403个精心注释的图像，具有58个细粒度语义类别标签，每个图像涉及2-26个人，并且在各种视点，姿势，遮挡，交互和背景的真实场景中捕获。我们进一步提出了一种用于多人解析的新型深嵌套对抗网络（NAN）模型。 NAN由三个类似生成对抗网络（GAN）的子网组成，分别执行语义显着性预测，实例不可知解析和实例感知聚类。这些子网形成一个嵌套结构，经过精心设计，以端到端的方式共同学习。 NAN在我们的MHP和其他几个数据集上始终优于现有的最先进解决方案，并作为推动未来多人解析研究的强大基线。
URL
https://arxiv.org/abs/1804.03287
