  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 https://github.com/Traffic-X/Open-TransMind.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning