In this paper, we propose a solution for cross-modal transportation
retrieval. Due to the cross-domain problem of traffic images, we divide the
problem into two sub-tasks of pedestrian retrieval and vehicle retrieval
through a simple strategy. In pedestrian retrieval tasks, we use IRRA as the
base model and specifically design an Attribute Classification to mine the
knowledge implied by attribute labels. More importantly, We use the strategy of
Inclusion Relation Matching to make the image-text pairs with inclusion
relation have similar representation in the feature space. For the vehicle
retrieval task, we use BLIP as the base model. Since aligning the color
attributes of vehicles is challenging, we introduce attribute-based object
detection techniques to add color patch blocks to vehicle images for color data
augmentation. This serves as strong prior information, helping the model
perform the image-text alignment. At the same time, we incorporate labeled
attributes into the image-text alignment loss to learn fine-grained alignment
and prevent similar images and texts from being incorrectly separated. Our
approach ranked first in the final B-board test with a score of 70.9.

本文提出了一种解决跨模态交通检索问题的方法，将问题分为行人检索和车辆检索两个子任务，并分别采用 IRRA 和 BLIP 作为基础模型，同时引入属性分类和基于属性的目标检测技术来增强图像和文本之间的对齐，取得了 70.9 的排名。