The Solution for the CVPR 2023 1st foundation model challenge-Track2
<|reference_start|>The Solution for the CVPR 2023 1st foundation model challenge-Track2: 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.<|reference_end|>
@article{xu2024the,
    title={The Solution for the CVPR 2023 1st foundation model challenge-Track2},
    author={Haonan Xu, Yurui Huang, Sishun Pan, Zhihao Guan, Yi Xu, Yang Yang},
    journal={arXiv preprint arXiv:2403.17702},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.17702},
    primaryClass={cs.CV}
}