Efficient multi-task progressive learning for semantic segmentation and disparity estimation

Hanz Cuevas-Velasquez, Alejandro Galán-Cuenca, Robert B. Fisher, Antonio Javier Gallego

Abstract: Highlights•End-to-end network that learns semantic segmentation and disparity from stereo data.•Progressive learning extracts task-specific features and multi-scale knowledge.•State-of-the-art results for multitasking co-learning of disparity and segmentation.•Competitive results against state-of-the-art single-task methods.•The network solves both tasks with less than 1/3 of the parameters of previous works.