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  • Open Access

    ARTICLE

    CGMISeg: Context-Guided Multi-Scale Interactive for Efficient Semantic Segmentation

    Ze Wang, Jin Qin, Chuhua Huang*, Yongjun Zhang*

    CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5811-5829, 2025, DOI:10.32604/cmc.2025.064537 - 30 July 2025

    Abstract Semantic segmentation has made significant breakthroughs in various application fields, but achieving both accurate and efficient segmentation with limited computational resources remains a major challenge. To this end, we propose CGMISeg, an efficient semantic segmentation architecture based on a context-guided multi-scale interaction strategy, aiming to significantly reduce computational overhead while maintaining segmentation accuracy. CGMISeg consists of three core components: context-aware attention modulation, feature reconstruction, and cross-information fusion. Context-aware attention modulation is carefully designed to capture key contextual information through channel and spatial attention mechanisms. The feature reconstruction module reconstructs contextual information from different scales, modeling… More >

  • Open Access

    ARTICLE

    Research on Sarcasm Detection Technology Based on Image-Text Fusion

    Xiaofang Jin1, Yuying Yang1,*, Yinan Wu1, Ying Xu2

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5225-5242, 2024, DOI:10.32604/cmc.2024.050384 - 20 June 2024

    Abstract The emergence of new media in various fields has continuously strengthened the social aspect of social media. Netizens tend to express emotions in social interactions, and many people even use satire, metaphors, and other techniques to express some negative emotions, it is necessary to detect sarcasm in social comment data. For sarcasm, the more reference data modalities used, the better the experimental effect. This paper conducts research on sarcasm detection technology based on image-text fusion data. To effectively utilize the features of each modality, a feature reconstruction output algorithm is proposed. This algorithm is based… More >

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