With the continuous improvement of computing power and deep learning
algorithms in recent years, the foundation model has grown in popularity.
Because of its powerful capabilities and excellent performance, this technology
is being adopted and applied by an increasing number of industries. In the
intelligent transportation industry, artificial intelligence faces the
following typical challenges: few shots, poor generalization, and a lack of
multi-modal techniques. Foundation model technology can significantly alleviate
the aforementioned issues. To address these, we designed the 1st Foundation
Model Challenge, with the goal of increasing the popularity of foundation model
technology in traffic scenarios and promoting the rapid development of the
intelligent transportation industry. The challenge is divided into two tracks:
all-in-one and cross-modal image retrieval. Furthermore, we provide a new
baseline and benchmark for the two tracks, called Open-TransMind. According to
our knowledge, Open-TransMind is the first open-source transportation
foundation model with multi-task and multi-modal capabilities. Simultaneously,
Open-TransMind can achieve state-of-the-art performance on detection,
classification, and segmentation datasets of traffic scenarios. Our source code
is available at this https URL

在智能交通领域，基于深度学习的 foundation model 技术面临着少样本、泛化性能差、缺乏多模态技术等挑战。为了促进基于 foundation model 的技术在交通场景的普及和智能交通产业的快速发展，我们设计并提出了首届 Foundation Model Challenge，其中包含全能型和跨模态图像检索两个实验任务。而我们还提供了一个新的基线和基准，名为 Open-TransMind。Open-TransMind 是第一个具有多任务和多模态能力的交通领域基础模型开源库，可以在交通场景的数据集上实现一流的检测、分类和分割性能。