# Weakly Supervised Semantic Segmentation Using Constrained Dominant Sets

Source: [Weakly Supervised Semantic Segmentation Using Constrained Dominant Sets](<https://www.sota2.com/research/paper/68fa665e1e209131cea0fdab>)

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

Data updated: 2025-10-25T21:48:29.028Z

Authors: Sinem Aslan, Marcello Pelillo

Published: 2019-09-20T10:32:48.000Z

## Abstract

The availability of large-scale data sets is an essential pre-requisite 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.

## 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.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1909.09414>)

[PDF](<https://arxiv.org/pdf/1909.09414.pdf>)