# Learning to Exploit the Prior Network Knowledge for Weakly-Supervised Semantic Segmentation

Source: [Learning to Exploit the Prior Network Knowledge for Weakly-Supervised Semantic Segmentation](<https://www.sota2.com/research/paper/68fa65651e209131cea09f16>)

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

Data updated: 2025-10-25T21:25:53.160Z

Authors: Carolina Redondo-Cabrera, Marcos Baptista-R\\'ios, Roberto J. L\\'opez-Sastre

Published: 2018-04-13T10:52:07.000Z

## Abstract

Training a Convolutional Neural Network (CNN) for semantic segmentation typically requires to collect a large amount of accurate pixel-level annotations, a hard and expensive task. In contrast, simple image tags are easier to gather. With this paper we introduce a novel weakly-supervised semantic segmentation model able to learn from image labels, and just image labels. Our model uses the prior knowledge of a network trained for image recognition, employing these image annotations as an attention mechanism to identify semantic regions in the images. We then present a methodology that builds accurate class-specific segmentation masks from these regions, where neither external objectness nor saliency algorithms are required. We describe how to incorporate this mask generation strategy into a fully end-to-end trainable process where the network jointly learns to classify and segment images. Our experiments on PASCAL VOC 2012 dataset show that exploiting these generated class-specific masks in conjunction with our novel end-to-end learning process outperforms several recent weakly-supervised semantic segmentation methods that use image tags only, and even some models that leverage additional supervision or training data.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1804.04882>)

[PDF](<https://arxiv.org/pdf/1804.04882.pdf>)