发表机构
Department of Computer Science, University of Verona; Institute of Marine Research; University of Agder, Centre for Coastal Research（维罗纳大学计算机科学系; 海洋研究所; 阿格德大学海岸研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对动物再识别中适应纵向生态环境的挑战，提出基于连续元数据条件的参数高效CLIP适配框架，通过保留元数据连续结构，在训练中平滑调制嵌入空间，提升了对外观变化和分布偏移的鲁棒性，实现纯视觉推理管道。
AI 中文摘要
长期的动物再识别必须对形态的逐渐演变和季节性外观变化保持稳健。尽管近期视觉语言模型提供了强大的预训练视觉表征，但使其适应纵向生态环境仍具挑战。我们提出了一个用于动物再识别的参数高效CLIP适配框架，并引入连续元数据条件机制，在训练期间将数值属性直接纳入提示表征。低秩视觉适配、基于提示的监督和跨模态对齐构成适配框架，元数据条件策略是主要方法贡献。通过保留数值元数据的连续结构，该方法在训练期间能平滑调制嵌入空间，同时保持纯视觉推理管道。在一个七年纵向鱼类数据集和多个野生动物基准上的实验表明，在封闭集、开放集和时间感知评估协议下性能得到提升。结果表明，连续元数据条件提高了对纵向外观变化和时间分布偏移的鲁棒性，而参数高效适配实现了纯视觉推理管道，测试时无需元数据。代码和评估分割可在指定链接获取。
英文摘要
Long-term animal re-identification (ReID) must remain robust to gradual morphological evolution and seasonal appearance shifts. Although recent vision-language models provide strong pretrained visual representations, adapting them to longitudinal ecological settings remains challenging, particularly under identity and temporal distribution shifts. We present a parameter-efficient CLIP adaptation framework for animal ReID and introduce a continuous metadata-conditioning mechanism that incorporates numerical attributes directly into the prompt representation during training. While low-rank visual adaptation, prompt-based supervision, and cross-modal alignment provide the adaptation framework, the proposed metadata-conditioning strategy constitutes the primary methodological contribution. By preserving the continuous structure of numerical metadata rather than discretizing it into textual categories, the proposed approach enables smooth modulation of the embedding space during training while maintaining a purely visual inference pipeline. Experiments on a seven-year longitudinal fish dataset and multiple wildlife benchmarks demonstrate improved performance under closed-set, open-set, and time-aware evaluation protocols. The results demonstrate that continuous metadata conditioning improves robustness to longitudinal appearance variation and temporal distribution shifts, while parameter-efficient adaptation enables a purely visual inference pipeline without requiring metadata at test time. Code and evaluation splits can be found at: https://github.com/AnilOsmanTur/MetaPrompt-ReID.
CommentsThis is the author's version of the paper accepted for publication in Expert Systems with Applications. The final authenticated version will be available from the publisher
