Convolutional neural networks (CNNs) based approaches for semantic alignment
and object landmark detection have improved their performance significantly.
Current efforts for the two tasks focus on addressing the lack of massive
training data through weakly- or unsupervised learning frameworks. In this
paper, we present a joint learning approach for obtaining dense correspondences
and discovering object landmarks from semantically similar images. Based on the
key insight that the two tasks can mutually provide supervisions to each other,
our networks accomplish this through a joint loss function that alternatively
imposes a consistency constraint between the two tasks, thereby boosting the
performance and addressing the lack of training data in a principled manner. To
the best of our knowledge, this is the first attempt to address the lack of
training data for the two tasks through the joint learning. To further improve
the robustness of our framework, we introduce a probabilistic learning
formulation that allows only reliable matches to be used in the joint learning
process. With the proposed method, state-of-the-art performance is attained on
several standard benchmarks for semantic matching and landmark detection,
including a newly introduced dataset, JLAD, which contains larger number of
challenging image pairs than existing datasets.

本文提出了一种基于卷积神经网络的联合学习方法，以在语义相似的图像中获得稠密对应和发现对象地标，在解决训练数据不足方面取得了显著成效，并引入了概率学习方法以提高框架的稳健性，该方法在语义匹配和地标检测的多个标准基准测试中取得了最新的最佳表现。