  Vehicle search is one basic task for the efficient traffic management in
terms of the AI City. Most existing practices focus on the image-based vehicle
matching, including vehicle re-identification and vehicle tracking. In this
paper, we apply one new modality, i.e., the language description, to search the
vehicle of interest and explore the potential of this task in the real-world
scenario. The natural language-based vehicle search poses one new challenge of
fine-grained understanding of both vision and language modalities. To connect
language and vision, we propose to jointly train the state-of-the-art vision
models with the transformer-based language model in an end-to-end manner.
Except for the network structure design and the training strategy, several
optimization objectives are also re-visited in this work. The qualitative and
quantitative experiments verify the effectiveness of the proposed method. Our
proposed method has achieved the 1st place on the 5th AI City Challenge,
yielding competitive performance 18.69% MRR accuracy on the private test set.
We hope this work can pave the way for the future study on using language
description effectively and efficiently for real-world vehicle retrieval
systems. The code will be available at
https://github.com/ShuaiBai623/AIC2021-T5-CLV.
