DIME：通过差分掩码自编码器扩展面部表征学习
DIME: Scaling Facial Representation Learning via Differential Masked Autoencoding
AI总结：
DIME是一种差分掩码自编码器，通过利用同身份图像的差分方向监督，在2000万张面部图像上预训练后，在多项面部分析任务上性能优于专用方法，且随数据扩展持续提升。
摘要：
扩大数据量和模型容量推动了视觉表征学习的显著进展，但传统的预训练任务在细粒度面部分析上的收益逐渐递减。我们将此归因于一个扩展瓶颈：这些任务对每张图像的外观进行建模，且受身份、姿态和光照主导，因此身份内部的细微面部差异对训练信号贡献极小。我们提出DIME，一种用于大规模面部表征学习的差分掩码自编码器。DIME基于一个简单观察：同一个人的两张图像共享身份特定的外观，因此它们的差异能暴露面部变化。DIME重建身份匹配对中的两张掩码图像，并通过显式差分方向监督（EDDS）目标，将其重建差异的方向与真实差异对齐，同时源隔离使每次重建仅依赖自身图像。这种差异中共享外观会抵消，且由于EDDS忽略幅度，细微面部变化与大姿态或光照变化同等重要。DIME仅使用身份标签形成预训练对，无需密集标注，且能学习具有涌现部件级语义的密集面部表征。我们在从现有面部数据集整合的2000万张高分辨率面部图像上预训练DIME。DIME的动作单元（AU）检测性能随预训练数据从600万张扩展到2000万张而持续提升，其在BP4D数据集上的F1值提升5.9个百分点，而在相同子集上的平均绝对误差（MAE）仅提升0.5个百分点。DIME在AU检测、AU强度估计、跨域反欺骗、关键点定位、头部姿态估计、面部解析和面部生成等任务上也优于专用面部方法。
英文摘要：
Scaling up data and model capacity has driven substantial progress in visual representation learning. However, conventional pretext tasks exhibit diminishing returns on fine-grained facial analysis. We attribute this to a scaling bottleneck: these tasks model each image's appearance and are dominated by identity, pose, and illumination, so subtle within-identity facial differences contribute little to the training signal. We introduce DIME, a differential masked autoencoder for large-scale facial representation learning. DIME builds on a simple observation: two images of the same person share identity-specific appearance, so their difference exposes facial changes. DIME reconstructs both masked images in an identity-matched pair and, through an Explicit Differential Direction Supervision (EDDS) objective, aligns the direction of their reconstructed difference with the true difference, while source isolation makes each reconstruction depend only on its own image. Shared appearance cancels in this difference, and because EDDS ignores magnitude, subtle facial changes count as much as large pose or illumination changes. DIME uses identity labels only to form pretraining pairs, requires no dense annotations, and learns dense facial representations with emergent part-level semantics. We pretrain DIME on 20 million high-resolution facial images consolidated from existing face datasets. DIME's AU detection performance continues to improve as pretraining data scales from 6M to 20M images, with its BP4D F1 increasing by 5.9 points, while MAE gains only 0.5 points on the same subsets. DIME also outperforms specialized facial methods on AU detection, AU intensity estimation, cross-domain anti-spoofing, landmark localization, head pose estimation, face parsing, and face generation.
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