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Found 3 papers, 0 papers with code
Weakly Supervised Instance Segmentation by Learning Annotation Consistent Instances
Recent approaches for weakly supervised instance segmentations depend on two components: (i) a pseudo label generation model that provides instances which are consistent with a given annotation; and (ii) an instance segmentation model, which is trained in a supervised manner using the pseudo labels as ground-truth.
Ranked #6 on
Image-level Supervised Instance Segmentation
on PASCAL VOC 2012 val
(using extra training data)
Image-level Supervised Instance Segmentation
Pseudo Label
+3
Dissimilarity Coefficient based Weakly Supervised Object Detection
This allows us to use a state of the art discrete generative model that can provide annotation consistent samples from the conditional distribution.
Learning Human Poses from Actions
In order to avoid the high cost of full supervision, we propose to use a diverse data set, which consists of two types of annotations: (i) a small number of images are labeled using the expensive ground-truth pose; and (ii) other images are labeled using the inexpensive action label.
