Abstract
We address the problem of semantic segmentation of objects in weakly supervised setting, when only image-wide labels are available. We describe an image with a set of pre-trained convolutional features and embed this set into a Fisher vector. We apply the learned image classifier on the set of all image regions and propagate the region scores back to the pixels. Compared to the alternatives the proposed method is simple, fast in inference, and especially in training. The method displays very good performance of on two standard semantic segmentation benchmarks.
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Acknowledgement
This work has been fully supported by Croatian Science Foundation under the project I-2433-2014.
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Krapac, J., Šegvić, S. (2016). Weakly-Supervised Semantic Segmentation by Redistributing Region Scores Back to the Pixels. In: Rosenhahn, B., Andres, B. (eds) Pattern Recognition. GCPR 2016. Lecture Notes in Computer Science(), vol 9796. Springer, Cham. https://doi.org/10.1007/978-3-319-45886-1_31
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DOI: https://doi.org/10.1007/978-3-319-45886-1_31
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