Abstract
Multi-human parsing is an image segmentation task necessitating both instance-level and fine-grained category-level information. However, prior research has typically processed these two types of information through separate branches and distinct output formats, leading to inefficient and redundant frameworks. This paper introduces UniParser, which integrates instance-level and category-level representations in three key aspects: 1) we propose a unified correlation representation learning approach, allowing our network to learn instance and category features within the cosine space; 2) we unify the form of outputs of each modules as pixel-level segmentation results while supervising instance and category features using a homogeneous label accompanied by an auxiliary loss; and 3) we design a joint optimization procedure to fuse instance and category representations. By virtual of unifying instance-level and category-level output, UniParser circumvents manually designed post-processing techniques and surpasses state-of-the-art methods, achieving 49.3% AP on MHPv2.0 and 60.4% AP on CIHP. We will release our source code, pretrained models, and online demos to facilitate future studies.
Abstract (translated)
多人类标注是一个需要同时考虑实例水平和细粒度类水平信息的图像分割任务。然而,先前的研究通常通过单独的分支和不同的输出格式处理这两种信息,导致效率低下和冗余的框架。本文介绍了一种名为UniParser的方法,将实例水平和类水平表示在三个关键方面进行整合:1)我们提出了一个统一的相关性表示学习方法,使得我们的网络能够在余弦空间中学习实例和类特征;2)我们通过统一的标签和辅助损失在实例和类特征上进行监督,实现了每个模块的输出形式为像素级分割结果;3)我们设计了一个联合优化程序来融合实例和类表示。通过统一实例水平和类水平输出,UniParser绕过了手动设计的后处理技术,超越了最先进的methods,实现了MHPv2.0上的49.3%AP和CIHP上的60.4%AP。我们将发布我们的源代码、预训练模型和在线演示,以促进未来的研究。
URL
https://arxiv.org/abs/2310.08984
