Imagine that I have a model that can do 2D landmark predictions for a moving deformable object (e.g. facial landmarks). How can I change this model such that it would work in a semi-supervised fashion in which I only have annotation for 500-1000 frames but I want the algorithm to make annotations on the rest of frames (in which rest of the frames don’t have groundtruth)?
- How the current model in the link should be changed?
- What evaluation score should be used since we don’t have the grountruth for rest of the frames and we can’t use something like MSELoss?
- Assume that the 500-1000 frames to be annotated is selected via a clustering algorithm such as K-means.
Here’s an example a fully supervised 2D facial landmark estimation problem: