  Natural language-based vehicle retrieval is a task to find a target vehicle
within a given image based on a natural language description as a query. This
technology can be applied to various areas including police searching for a
suspect vehicle. However, it is challenging due to the ambiguity of language
descriptions and the difficulty of processing multi-modal data. To tackle this
problem, we propose a deep neural network called SBNet that performs natural
language-based segmentation for vehicle retrieval. We also propose two
task-specific modules to improve performance: a substitution module that helps
features from different domains to be embedded in the same space and a future
prediction module that learns temporal information. SBnet has been trained
using the CityFlow-NL dataset that contains 2,498 tracks of vehicles with three
unique natural language descriptions each and tested 530 unique vehicle tracks
and their corresponding query sets. SBNet achieved a significant improvement
over the baseline in the natural language-based vehicle tracking track in the
AI City Challenge 2021.
