AI 中文总结
针对高分辨率植物成像中基础模型域差距问题，提出可扩展的域内自监督预训练方法，显著提升稠密表示迁移性能。
AI 中文摘要
高分辨率植物成像能够实现对植物形态的详细表征，但稠密科学分析仍受限于昂贵的像素级标注、巨大的图像像素尺寸以及成像条件的显著变化。本研究提出了一种可扩展的域内自监督预训练基础模型，用于高分辨率、高像素维度的多物种植物图像。该模型采用基于ViT骨干的掩码自编码器，在超过1000万张多视角植物图像块上使用分布式训练进行预训练。在可扩展的预训练之后，学习到的基础模型表示在细粒度稠密预测和粗粒度全局特征识别上得到了全面基准测试，特别关注有限监督和现实下游成像条件。本研究聚焦于现有基础模型在稠密特征表示和迁移中的域差距。通过涉及有限标注、跨视角变化和分辨率退化的广泛实验，域内基础模型实现了平均Dice为0.8686、合并Dice为0.8959，分别比在大规模自然图像数据上预训练的MAE对应模型高出0.0694和0.0613。结果进一步表明，增加预训练规模可在稠密特征迁移中产生持续改进。总体而言，这些发现表明，扩展域内自监督预训练可以减少域差距，并改善高像素维度科学成像的可迁移稠密表示。
英文摘要
High-resolution plant imaging enables detailed characterization of plant morphology, but dense scientific analysis remains limited by costly pixel-level annotations, large image pixel dimensions, and substantial variation in imaging conditions. This work proposes a scalable in-domain self-supervised pretrained foundation model for high-resolution, high-pixel-dimension multi-species plant imagery. A masked autoencoder with a ViT backbone is pretrained on more than 10 million multi-view plant image tiles using distributed training. Following scalable pretraining, the learned foundation-model representations are comprehensively benchmarked across fine-grained dense prediction and coarse global feature recognition, with particular emphasis on limited supervision and realistic downstream imaging conditions. This work focuses on the domain gap of existing foundation models in dense feature representation and transfer. Through extensive experiments involving limited annotations, cross-view variation, and resolution degradation, the in-domain FM achieves a Mean Dice of 0.8686 and a Pooled Dice of 0.8959, outperforming an MAE counterpart pretrained on large-scale natural-image data by 0.0694 and 0.0613, respectively. The results further indicate that increasing pretraining scale produces consistent improvements in dense feature transfer. Overall, these findings suggest that scaling in-domain self-supervised pretraining can reduce the domain gap and improve transferable dense representations for high-pixel-dimension scientific imaging.
