# Incorporating Network Built-in Priors in Weakly-supervised Semantic Segmentation

## TL;DR

This paper introduces a novel method for weakly-supervised semantic segmentation that leverages network-derived masks from pre-trained recognition models, achieving state-of-the-art results without external objectness modules.

## Contribution

It proposes extracting accurate foreground/background masks directly from pre-trained networks and fusing them with localization information for improved segmentation.

## Key findings

- Achieves state-of-the-art weakly-supervised segmentation performance.
- Demonstrates masks can be extracted from higher-level convolutional layers.
- Fusion of masks with localization info improves accuracy.

## Abstract

Pixel-level annotations are expensive and time consuming to obtain. Hence, weak supervision using only image tags could have a significant impact in semantic segmentation. Recently, CNN-based methods have proposed to fine-tune pre-trained networks using image tags. Without additional information, this leads to poor localization accuracy. This problem, however, was alleviated by making use of objectness priors to generate foreground/background masks. Unfortunately these priors either require pixel-level annotations/bounding boxes, or still yield inaccurate object boundaries. Here, we propose a novel method to extract accurate masks from networks pre-trained for the task of object recognition, thus forgoing external objectness modules. We first show how foreground/background masks can be obtained from the activations of higher-level convolutional layers of a network. We then show how to obtain multi-class masks by the fusion of foreground/background ones with information extracted from a weakly-supervised localization network. Our experiments evidence that exploiting these masks in conjunction with a weakly-supervised training loss yields state-of-the-art tag-based weakly-supervised semantic segmentation results.