Simple Does It: Weakly Supervised Instance and Semantic Segmentation

Anna Khoreva, Rodrigo Benenson, Jan Hosang, Matthias Hein, Bernt Schiele

2016-03-24CVPR 2017 7SegmentationSemantic SegmentationInstance Segmentation
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Abstract

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 a new approach that does not require modification of the segmentation training procedure. We show that when carefully designing the input labels from given bounding boxes, even a single round of training is enough to improve over previously reported weakly supervised results. Overall, our weak supervision approach reaches ~95% of the quality of the fully supervised model, both for semantic labelling and instance segmentation.

Results

TaskDatasetMetricValueModel
Semantic SegmentationPASCAL VOC 2012 valMean IoU71.6SID
10-shot image generationPASCAL VOC 2012 valMean IoU71.6SID

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