ISIM: Iterative Self-Improved Model for Weakly Supervised Segmentation

• URL: http://arxiv.org/abs/2211.12455v2
• Date: Wed, 23 Nov 2022 07:00:26 GMT
• Title: ISIM: Iterative Self-Improved Model for Weakly Supervised Segmentation
• Authors: Cenk Bircanoglu, Nafiz Arica
• Abstract summary: Weakly Supervised Semantic Conditional (WSSS) is a challenging task aiming to learn the segmentation labels from class-level labels. We propose a framework that employs an iterative approach in a modified encoder-decoder-based segmentation model. Experiments performed with DeepLabv3 and UNet models show a significant gain on the Pascal VOC12 dataset.
• Score: 0.34265828682659694
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Weakly Supervised Semantic Segmentation (WSSS) is a challenging task aiming to learn the segmentation labels from class-level labels. In the literature, exploiting the information obtained from Class Activation Maps (CAMs) is widely used for WSSS studies. However, as CAMs are obtained from a classification network, they are interested in the most discriminative parts of the objects, producing non-complete prior information for segmentation tasks. In this study, to obtain more coherent CAMs with segmentation labels, we propose a framework that employs an iterative approach in a modified encoder-decoder-based segmentation model, which simultaneously supports classification and segmentation tasks. As no ground-truth segmentation labels are given, the same model also generates the pseudo-segmentation labels with the help of dense Conditional Random Fields (dCRF). As a result, the proposed framework becomes an iterative self-improved model. The experiments performed with DeepLabv3 and UNet models show a significant gain on the Pascal VOC12 dataset, and the DeepLabv3 application increases the current state-of-the-art metric by %2.5. The implementation associated with the experiments can be found: https://github.com/cenkbircanoglu/isim.

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