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<p>Weakly Supervised Semantic Segmentation (WSSS) aims to achieve pixellevel scene understanding using coarse-grained annotations such as imagelevel labels, thereby reducing the reliance on expensive pixel-level supervision. However, existing methods still face challenges such as incomplete object activation and background confusion. To address these issues, this paper proposes Dual-Axis and Group-normalized Network (DAGNet), an end-to-end framework based on Contrastive Language-Image Pretraining (CLIP), to enhance feature representation and pseudo-label quality. DAGNet integrates two core modules: the Dual-Axis Attention Fusion Module (DAAF), which achieves semantic-consistent feature fusion through adaptive modeling of channelspatial dual-axis attention; and the Grouped Spatial Normalization Module (GSN), which optimizes spatial saliency and enhances fine-grained context awareness. Furthermore, this paper introduces a collaborative optimization strategy to further stabilize the training process and suppress pseudo-label noise. Extensive experiments demonstrate that DAGNet achieves the current state-of-the-art performance without additional supervision, improving mIoU by 2.0% and 1.0% on PASCAL VOC 2012 and MS COCO 2014 datasets, respectively, compared to Weakly-supervised Semantic Segmentation with CLIP (WeCLIP), validating the effectiveness and robustness of the proposed method. The code is available at <a href="https://github.com/xm24080854037-eng/DAGNet.git">https://github.com/xm24080854037-eng/DAGNet.git</a></p>

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Dagnet: dual-axis attention and grouped normalization for enhanced CLIP-based weakly supervised semantic segmentation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月10日

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  • Xiaoyan Shao,
  • Xiaoming Bai,
  • Lingling Li,
  • Suqiao Li,
  • Jiaqi Han,
  • Zhenhao Zhao

摘要

Weakly Supervised Semantic Segmentation (WSSS) aims to achieve pixellevel scene understanding using coarse-grained annotations such as imagelevel labels, thereby reducing the reliance on expensive pixel-level supervision. However, existing methods still face challenges such as incomplete object activation and background confusion. To address these issues, this paper proposes Dual-Axis and Group-normalized Network (DAGNet), an end-to-end framework based on Contrastive Language-Image Pretraining (CLIP), to enhance feature representation and pseudo-label quality. DAGNet integrates two core modules: the Dual-Axis Attention Fusion Module (DAAF), which achieves semantic-consistent feature fusion through adaptive modeling of channelspatial dual-axis attention; and the Grouped Spatial Normalization Module (GSN), which optimizes spatial saliency and enhances fine-grained context awareness. Furthermore, this paper introduces a collaborative optimization strategy to further stabilize the training process and suppress pseudo-label noise. Extensive experiments demonstrate that DAGNet achieves the current state-of-the-art performance without additional supervision, improving mIoU by 2.0% and 1.0% on PASCAL VOC 2012 and MS COCO 2014 datasets, respectively, compared to Weakly-supervised Semantic Segmentation with CLIP (WeCLIP), validating the effectiveness and robustness of the proposed method. The code is available at https://github.com/xm24080854037-eng/DAGNet.git