这篇论文用SAM掩码改进了LeJEPA，让自监督学习在数据少时更管用。跟踪、分类、分割结果都比原版强，适合想提升视觉特征效率的同学看。
LeJEPA图像编码器需大型数据集，对象级对齐更高效但存在不稳定循环依赖。作者使用低成本SAM提案提供对象掩码，扩展LeJEPA从整图对齐到可变大小对象集。额外实例分离损失将同场景其他对象作为负样本，提升下游性能。在两个模型规模和10-100% COCO数据下，对象级LeJEPA在DAVIS跟踪、ImageNet-1k分类、ADE20k分割和NAVI重识别上均优于图像级LeJEPA。
Object-centric LeJEPA
Image encoders trained with LeJEPA can deliver strong features for downstream tasks, but, like other image-level self-supervised methods, typically require large training datasets. Aligning representations at the level of objects rather than whole scenes promises greater data efficiency, but doing this in a completely self-supervised way, effectively jointly partitioning a scene and representing its objects, is unstable: the two are locked in a cyclic dependency, partitioning requires meaningful representations, while meaningful representations require consistent partitioning. We sidestep this instability by taking object masks as given during training, using cheap, off-the-shelf SAM proposals. We extend LeJEPA - whose distributional anti-collapse objective ports naturally from whole images to variable-sized sets of objects - to align object-centric representations rather than whole images. An additional instance-separating loss, which treats other objects in the same scene as negatives, further boosts downstream performance. Across two model scales and 10-100% of COCO, object-level LeJEPA outperforms image-level LeJEPA on tracking (DAVIS), classification (ImageNet-1k), segmentation (ADE20k), and re-identification (NAVI).