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

[2604.10870] Semi-Supervised Goal-Oriented Semantic Communication Framework for Foreground Classification Skip to main content Search Submit Donate Log in Electrical Engineering and Systems Science > Image and Video Processing arXiv:2604.10870 (eess) [Submitted on 13 Apr 2026 ( v1 ), last revised 25 Aug 2026 (this version, v2)] Title: Semi-Supervised Goal-Oriented Semantic Communication Framework for Foreground Classification Authors: Zhitong Ni , Yansha Deng , Jinhong Yuan View PDF HTML (experimental) 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. Subjects: Image and Video Processing (eess.IV) Cite as: arXiv:2604.10870 [eess.IV] (or arXiv:2604.10870v2 [eess.IV] for this version) https://doi.org/10.48550/arXiv.2604.10870 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Zhitong Ni [ view email ] [v1] Mon, 13 Apr 2026 00:40:28 UTC (704 KB) [v2] Tue, 25 Aug 2026 03:29:25 UTC (704 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: eess.IV < prev | next > new | recent | 2026-04 Change to browse by: eess References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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