论文标题
较细的粒度分析物体：调查
Parsing Objects at a Finer Granularity: A Survey
论文作者
论文摘要
细粒度的视觉解析，包括细粒部分细分和细粒对象识别，由于其在许多现实世界应用中的重要性，例如农业，遥感和太空技术，引起了极大的关注。在不同的范式之后，主要的研究工作解决了这些细粒度的子任务，而这些任务之间的固有关系被忽略了。此外，鉴于大多数研究仍然分散，我们从学习零件关系的新角度进行了对高级工作的深入研究。从这个角度来看，我们首先将最新的研究和基准合成与新的分类法合并。基于此合并，我们重新审视了细粒度部分细分和识别任务中的普遍挑战，并通过部分关系学习这些重要挑战，提出了新的解决方案。此外，我们在细粒度的视觉解析中结束了一些有希望的研究线，以供未来的研究。
Fine-grained visual parsing, including fine-grained part segmentation and fine-grained object recognition, has attracted considerable critical attention due to its importance in many real-world applications, e.g., agriculture, remote sensing, and space technologies. Predominant research efforts tackle these fine-grained sub-tasks following different paradigms, while the inherent relations between these tasks are neglected. Moreover, given most of the research remains fragmented, we conduct an in-depth study of the advanced work from a new perspective of learning the part relationship. In this perspective, we first consolidate recent research and benchmark syntheses with new taxonomies. Based on this consolidation, we revisit the universal challenges in fine-grained part segmentation and recognition tasks and propose new solutions by part relationship learning for these important challenges. Furthermore, we conclude several promising lines of research in fine-grained visual parsing for future research.
