Symmetric Network with Spatial Relationship Modeling for Natural Language-based Vehicle Retrieval
Chuyang Zhao, Haobo Chen, Wenyuan Zhang, Junru Chen, Sipeng Zhang, Yadong Li, Boxun Li
Abstract
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 pre-served. 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 4th place on the public leaderboard. The code will be available at https://github.com/hbchen121/AICITY2022_Track2_SSM.