Weakly Supervised Semantic Labelling and Instance Segmentation
Anna Khoreva, Rodrigo Benenson, Jan Hosang, Matthias Hein, Bernt Schiele · arXiv (Cornell University) · 2016
Semantic labelling and instance segmentation are two tasks that require particularly costly annotations. Starting from weak supervision in the form of bounding box detection annotations, we propose to recursively train a convnet such that outputs are improved after each iteration. We explore which aspects affect the recursive training, and which is the most suitable box-guided segmentation to use as initialisation. Our results improve significantly over previously reported ones, even when using rectangles as rough initialisation. Overall, our weak supervision approach reaches ~95% of the quality of the fully supervised model, both for semantic labelling and instance segmentation.