Natural language (NL) based vehicle retrieval aims to search specific vehicle
given text description. Different from the image-based vehicle retrieval,
NL-based vehicle retrieval requires considering not only vehicle appearance,
but also surrounding environment and temporal relations. In this paper, we
propose a Symmetric Network with Spatial Relationship Modeling (SSM) method for
NL-based vehicle retrieval. Specifically, we design a symmetric network to
learn the unified cross-modal representations between text descriptions and
vehicle images, where vehicle appearance details and vehicle trajectory global
information are preserved. Besides, to make better use of location information,
we propose a spatial relationship modeling methods to take surrounding
environment and mutual relationship between vehicles into consideration. The
qualitative and quantitative experiments verify the effectiveness of the
proposed method. We achieve 43.92% MRR accuracy on the test set of the 6th AI
City Challenge on natural language-based vehicle retrieval track, yielding the
1st place among all valid submissions on the public leaderboard. The code is
available at this https URL

本研究提出了一种基于自然语言的车辆检索方法，使用对称网络结构和空间关系建模来保留车辆外貌细节和车辆轨迹全局信息，并考虑了车辆周围环境和车辆间的相互关系，实验结果在自然语言 - based 车辆检索竞赛中取得了最佳表现。