论文标题
GMNET：野外大规模零件语义分割的图形匹配网络
GMNet: Graph Matching Network for Large Scale Part Semantic Segmentation in the Wild
论文作者
论文摘要
野外对象部分的语义分割是一项艰巨的任务，其中必须在场景中检测到这些对象中的多个对象和多个部分。如今，尽管对详细的对象理解的重要性至关重要，但现在仍然非常详尽地探索了这个问题。在这项工作中，我们提出了一个新颖的框架，结合了更高的对象级别上下文调理和部分空间关系以解决任务。为了解决对象级别的歧义，在学习零件级别的语义时，引入了一个类调节模块，以保留类级语义。通过这种方式，中级功能在解码阶段之前还带有此信息。为了解决零件级别的歧义和本地化，我们提出了一个新型的基于图形图的模块，旨在匹配地面真相与预测部分之间的相对空间关系。 Pascal-Part数据集的实验评估表明，我们在此任务上实现了最新的结果。
The semantic segmentation of parts of objects in the wild is a challenging task in which multiple instances of objects and multiple parts within those objects must be detected in the scene. This problem remains nowadays very marginally explored, despite its fundamental importance towards detailed object understanding. In this work, we propose a novel framework combining higher object-level context conditioning and part-level spatial relationships to address the task. To tackle object-level ambiguity, a class-conditioning module is introduced to retain class-level semantics when learning parts-level semantics. In this way, mid-level features carry also this information prior to the decoding stage. To tackle part-level ambiguity and localization we propose a novel adjacency graph-based module that aims at matching the relative spatial relationships between ground truth and predicted parts. The experimental evaluation on the Pascal-Part dataset shows that we achieve state-of-the-art results on this task.
