ausblenden:
Schlagwörter:
Computer Science, Computer Vision and Pattern Recognition, cs.CV
Zusammenfassung:
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.
