# Closed-Loop Adaptation for Weakly-Supervised Semantic Segmentation

## TL;DR

This paper introduces a closed-loop self-adaptation mechanism for weakly-supervised semantic segmentation, leveraging feedback chains to improve seed generation and region expansion, resulting in better performance and efficiency.

## Contribution

It proposes a novel feedback-based framework that enhances existing methods by dynamically refining seeds and regions through two feedback chains.

## Key findings

- Outperforms state-of-the-art methods on PASCAL VOC 2012
- Reduces computational and memory requirements
- Improves segmentation accuracy through feedback loops

## Abstract

Weakly-supervised semantic segmentation aims to assign each pixel a semantic category under weak supervisions, such as image-level tags. Most of existing weakly-supervised semantic segmentation methods do not use any feedback from segmentation output and can be considered as open-loop systems. They are prone to accumulated errors because of the static seeds and the sensitive structure information. In this paper, we propose a generic self-adaptation mechanism for existing weakly-supervised semantic segmentation methods by introducing two feedback chains, thus constituting a closed-loop system. Specifically, the first chain iteratively produces dynamic seeds by incorporating cross-image structure information, whereas the second chain further expands seed regions by a customized random walk process to reconcile inner-image structure information characterized by superpixels. Experiments on PASCAL VOC 2012 suggest that our network outperforms state-of-the-art methods with significantly less computational and memory burden.