1. One major risk of methods that exploit relationships between action units is that the relationships can be very different accross datasets (e.g. AU6 can occur both in an expression of pain and in happiness, and this co-occurence will be very different in a positive salience dataset such as SEMAINE compared to something like UNBC pain dataset). This difference in correlation can already be seen in Figure 1 with quite different co-occurences of AU1 and AU12. A good way to test the generalization of such work is by performing cross-dataset experiments, which this paper is lacking. 2. The language in the paper is sometimes conversational and not scientific (use of terms like massive), and there are several opinions and claims that are not substantiated (e.g. "... facial landmarks, which are helpful for the recognition of AUs defined in small regions"), the paper could benefit from copy-editing 3. Why are two instances of the same network (resnet) are used as different views? Would using a different architecture instead be considered a more differing view? Would be great to see a justification for using two resnet networks. 4. Why is the approach limited to two views, it feels like the system should be able to generalize to more views without too much difficulty? Minor comments: - What is PCA style guarantee? - What is v in equation 2? - why are dfferent numbers of unlabeled images using in training BP4D and EmotioNet models? Trivia: massive face images -> large datasets donates -> denotes (x2) adjacent -> adjacency
1. One major risk of methods that exploit relationships between action units is that the relationships can be very different accross datasets (e.g. AU6 can occur both in an expression of pain and in happiness, and this co-occurence will be very different in a positive salience dataset such as SEMAINE compared to something like UNBC pain dataset). This difference in correlation can already be seen in Figure 1 with quite different co-occurences of AU1 and AU12. A good way to test the generalization of such work is by performing cross-dataset experiments, which this paper is lacking.