Yayınlanmış 1 Ocak 2019
| Sürüm v1
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Açık
Weakly Supervised Semantic Segmentation Using Constrained Dominant Sets
Oluşturanlar
Açıklama
The availability of large-scale data sets is an essential prerequisite for deep learning based semantic segmentation schemes. Since obtaining pixel-level labels is extremely expensive, supervising deep semantic segmentation networks using low-cost weak annotations has been an attractive research problem in recent years. In this work, we explore the potential of Constrained Dominant Sets (CDS) for generating multi-labeled full mask predictions to train a fully convolutional network (FCN) for semantic segmentation. Our experimental results show that using CDS's yields higher-quality mask predictions compared to methods that have been adopted in the literature for the same purpose.
Dosyalar
bib-c2fe67a8-1035-4047-b26e-5e7c08b19b16.txt
Dosyalar
(154 Bytes)
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md5:7db7a826c463168a2758833f3700ade5
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154 Bytes | Ön İzleme İndir |