Removing supervision in semantic segmentation with local-global matching and area balancing

• URL: http://arxiv.org/abs/2303.17410v1
• Date: Thu, 30 Mar 2023 14:27:42 GMT
• Title: Removing supervision in semantic segmentation with local-global matching and area balancing
• Authors: Simone Rossetti (1 and 2), Nico Sam\`a (1), Fiora Pirri (1 and 2) ((1) DeepPlants, (2) Diag Sapienza)
• Abstract summary: We design a novel end-to-end model leveraging local-global patch matching to predict categories, good localization, area and shape of objects for semantic segmentation. Our model attains state-of-the-art in Weakly Supervised Semantic, only image-level labels, with 75% mIoU on PascalVOC2012 val set and 46% on MS-COCO2014 val set. We also attain state-of-the-art on Unsupervised Semantic with 43.6% mIoU on PascalVOC2012 val set and 19.4% on MS-COCO2014 val set.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Removing supervision in semantic segmentation is still tricky. Current approaches can deal with common categorical patterns yet resort to multi-stage architectures. We design a novel end-to-end model leveraging local-global patch matching to predict categories, good localization, area and shape of objects for semantic segmentation. The local-global matching is, in turn, compelled by optimal transport plans fulfilling area constraints nearing a solution for exact shape prediction. Our model attains state-of-the-art in Weakly Supervised Semantic Segmentation, only image-level labels, with 75% mIoU on PascalVOC2012 val set and 46% on MS-COCO2014 val set. Dropping the image-level labels and clustering self-supervised learned features to yield pseudo-multi-level labels, we obtain an unsupervised model for semantic segmentation. We also attain state-of-the-art on Unsupervised Semantic Segmentation with 43.6% mIoU on PascalVOC2012 val set and 19.4% on MS-COCO2014 val set. The code is available at https://github.com/deepplants/PC2M.

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