Deep Learning, Artificial Intelligence, Semantic Segmentation, Weakly Supervised, Semi Supervised Learning.
possible foreground labels and M is the number of pixels. 1.2. Fully-Supervised Setting Fully-Supervised Setting In the fully supervised case above, the objective function is: where θ is the vector of model parameters. The per-pixel label distributions are computed by: where fm(ym|x,θ) is the model