# ISIM: Iterative Self-Improved Model for Weakly Supervised Segmentation

Source: [ISIM: Iterative Self-Improved Model for Weakly Supervised Segmentation](<https://www.sota2.com/research/paper/68fa6dfe1e209131cea3d3d3>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-04T04:15:13.866Z

Authors: Cenk Bircanoglu, Nafiz Arica

Published: 2022-11-22T18:14:06.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Weakly Supervised Semantic Segmentation on PASCAL VOC 2012 (val)](<https://www.sota2.com/research/sota/weakly-supervised-semantic-segmentation-on-pascal-voc-2012-val.md>): Weakly supervised semantic segmentation; PASCAL VOC 2012 (val); mIoU: not recorded.

- [Weakly Supervised Semantic Segmentation on PASCAL VOC 2012 (test)](<https://www.sota2.com/research/sota/weakly-supervised-semantic-segmentation-on-pascal-voc-2012-test.md>): Weakly supervised semantic segmentation; PASCAL VOC 2012 (test); mIoU: not recorded.

- [Weakly Supervised Semantic Segmentation on PASCAL VOC 2012 (train)](<https://www.sota2.com/research/sota/weakly-supervised-semantic-segmentation-on-pascal-voc-2012-train.md>): Weakly supervised semantic segmentation; PASCAL VOC 2012 (train); mIoU: not recorded.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2211.12455>)

[PDF](<https://arxiv.org/pdf/2211.12455.pdf>)

[Implementation](<https://github.com/cenkbircanoglu/isim>)