Semi-Supervised Goal-Oriented Semantic Communication Framework for Foreground Classification

TL;DR

This paper introduces a semi-supervised goal-oriented semantic communication framework that efficiently transmits and classifies foreground objects in images, reducing data size and manual labeling needs.

Contribution

It develops a novel semi-supervised framework with foreground-aware autoencoders for efficient, accurate image foreground classification in wireless communication.

Findings

Achieves over 90% classification accuracy

Reduces image data size by 95%

Requires minimal manual labeling

Abstract

Wireless goal-oriented semantic communication (GSC) has emerged as a promising paradigm by directly optimizing task performance. However, existing GSC frameworks typically operate on entire images and rely on labeled data for classification tasks, which can limit their compression efficiency and increase the risk of overfitting. This paper proposes a novel semi-supervised wireless GSC framework for the unlabeled image foreground classification task. In our proposed framework, a foreground-aware masked autoencoder (MAE) is developed to prioritize semantically important foreground objects, thereby reducing transmission overhead. To enable accurate reconstruction and classification under a limited data size, we further propose a semi-supervised autoencoder (SSAE) that decodes the semantic latent tensor and refines image details by leveraging three complementary information sources, followed by fine-tuning a pre-trained image classification model. The entire pipeline, from foreground masking to classification, is trained in a semi-supervised manner to significantly reduce the need for manual labeling. Simulation results validate that the proposed GSC framework achieves over 90% image classification accuracy while reducing the original image data size by 95%, and demonstrate its strong potential for practical tasks in resource-constrained wireless scenarios.