Cross-Platform Video Person ReID: A New Benchmark Dataset and Adaptation Approach
<|reference_start|>Cross-Platform Video Person ReID: A New Benchmark Dataset and Adaptation Approach: In this paper, we construct a large-scale benchmark dataset for Ground-to-Aerial Video-based person Re-Identification, named G2A-VReID, which comprises 185,907 images and 5,576 tracklets, featuring 2,788 distinct identities. To our knowledge, this is the first dataset for video ReID under Ground-to-Aerial scenarios. G2A-VReID dataset has the following characteristics: 1) Drastic view changes; 2) Large number of annotated identities; 3) Rich outdoor scenarios; 4) Huge difference in resolution. Additionally, we propose a new benchmark approach for cross-platform ReID by transforming the cross-platform visual alignment problem into visual-semantic alignment through vision-language model (i.e., CLIP) and applying a parameter-efficient Video Set-Level-Adapter module to adapt image-based foundation model to video ReID tasks, termed VSLA-CLIP. Besides, to further reduce the great discrepancy across the platforms, we also devise the platform-bridge prompts for efficient visual feature alignment. Extensive experiments demonstrate the superiority of the proposed method on all existing video ReID datasets and our proposed G2A-VReID dataset.<|reference_end|>
@article{zhang2024cross-platform,
    title={Cross-Platform Video Person ReID: A New Benchmark Dataset and Adaptation
  Approach},
    author={Shizhou Zhang, Wenlong Luo, De Cheng, Qingchun Yang, Lingyan Ran,
  Yinghui Xing, and Yanning Zhang},
    journal={arXiv preprint arXiv:2408.07500},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.07500},
    primaryClass={cs.CV}
}