Many recent 6D pose estimation methods exploited object 3D models to generate synthetic images for training because labels come for free. However, due to the domain shift of data distributions between real images and synthetic images, the network trained only on synthetic images fails to capture robust features in real images for 6D pose estimation. We propose to solve this problem by making the network insensitive to different domains, rather than taking the more difficult route of forcing synthetic images to be similar to real images. Inspired by domain adaption methods, a Domain Adaptive Keypoints Detection Network (DAKDN) including a domain adaption layer is used to minimize the discrepancy of deep features between synthetic and real images. A unique challenge here is the lack of ground truth labels (i.e., keypoints) for real images. Fortunately, the geometry relations between keypoints are invariant under real/synthetic domains. Hence, we propose to use the domain-invariant geometry structure among keypoints as a "bridge" constraint to optimize DAKDN for 6D pose estimation across domains. Specifically, DAKDN employs a Graph Convolutional Network (GCN) block to learn the geometry structure from synthetic images and uses the GCN to guide the training for real images. The 6D poses of objects are calculated using Perspective-n-Point (PnP) algorithm based on the predicted keypoints. Experiments show that our method outperforms state-of-the-art approaches without manual poses labels and competes with approaches using manual poses labels.

Similar to the basic facial expression recognition, one challenge for pain intensity recognition is some individual characteristics, e.g. face shapes, may cause great diversities in the same emotion. So it is usually very difficult to distinguish two adjacent intensity levels of pain expression as each intensity has a large variation. In this study, a coarse‐to‐fine combination method is proposed for pain intensity recognition. The results of multi‐scale outputs from multiple base deep network are combined in a probabilistic way for improving the discrimination between visually similar adjacent levels. A two‐layer tree classifier is proposed in a multi‐task framework for pain intensity recognition as well as face shape recognition, replacing the planar Softmax classifier in each base deep network. In the first layer of tree classifier, multi‐scale classifiers are constructed for recognizing facial pain intensities and the conventional classifiers are constructed for face shape recognition in the second layer. Finally, the tree classifier including multi‐scale classifiers and conventional classifiers is jointly optimised during the training phase and only high level classifiers are used for recognising pain intensities in the test phase. The extensive experiments on UNBC shoulder pain dataset show the proposed method gets promising results in pain intensity recognition.