Symmetric Network with Spatial Relationship Modeling for Natural Language-based Vehicle Retrieval
| Autor: | Zhao, Chuyang, Chen, Haobo, Zhang, Wenyuan, Chen, Junru, Zhang, Sipeng, Li, Yadong, Li, Boxun |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 3226-3233 |
| Druh dokumentu: | Working Paper |
| Popis: | 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 https://github.com/hbchen121/AICITY2022_Track2_SSM. Comment: 8 pages, 3 figures, publised to CVPRW |
| Databáze: | arXiv |
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