We propose Probabilistic Warp Consistency, a weakly-supervised learning
objective for semantic matching. Our approach directly supervises the dense
matching scores predicted by the network, encoded as a conditional probability
distribution. We first construct an image triplet by applying a known warp to
one of the images in a pair depicting different instances of the same object
class. Our probabilistic learning objectives are then derived using the
constraints arising from the resulting image triplet. We further account for
occlusion and background clutter present in real image pairs by extending our
probabilistic output space with a learnable unmatched state. To supervise it,
we design an objective between image pairs depicting different object classes.
We validate our method by applying it to four recent semantic matching
architectures. Our weakly-supervised approach sets a new state-of-the-art on
four challenging semantic matching benchmarks. Lastly, we demonstrate that our
objective also brings substantial improvements in the strongly-supervised
regime, when combined with keypoint annotations.

本文提出了概率 Warp 一致性，用于语义匹配的弱监督学习目标。我们的方法直接监督由网络预测的密集匹配分数，并在概率分布中编码。我们通过应用已知的 Warp 构造图像三元组来推导出我们的概率学习目标，然后使用由此产生的图像三元组的约束来推导出概率学习目标。