Multi-instance methods for partially supervised image segmentation
PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
Joint semantic segmentation by searching for compatible-competitive references
Proceedings of the 20th ACM international conference on Multimedia
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Sparse reconstruction for weakly supervised semantic segmentation
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We propose a novel method for weakly supervised semantic segmentation. Training images are labeled only by the classes they contain, not by their location in the image. On test images instead, the method predicts a class label for every pixel. Our main innovation is a multi-image model (MIM) - a graphical model for recovering the pixel labels of the training images. The model connects superpixels from all training images in a data-driven fashion, based on their appearance similarity. For generalizing to new test images we integrate them into MIM using a learned multiple kernel metric, instead of learning conventional classifiers on the recovered pixel labels. We also introduce an "objectness" potential, that helps separating objects (e.g. car, dog, human) from background classes (e.g. grass, sky, road). In experiments on the MSRC 21 dataset and the LabelMe subset of [18], our technique outperforms previous weakly supervised methods and achieves accuracy comparable with fully supervised methods.