Scholarlink

<p>The core challenge in image-level weakly supervised semantic segmentation lies in generating high-quality object localization maps from simple image labels. Class Activation Map (CAM) produced by existing methods commonly suffer from two major flaws: incomplete coverage of target regions and severe background interference. To address these issues, we present a CAM-native perception-optimization framework for weakly supervised semantic segmentation. First, design a CAM generation mechanism guided by image-level weak supervision, which refines activated regions via discriminative region enhancement and spatial noise suppression. This process promotes fine-grained pixel clustering and improves the completeness of object localization. Second, introduce a spatial cue generator to enhance the adaptability of class representations, coupled with an inter-class relation propagation module that explicitly models inter-class relationships to suppress erroneous activations and significantly reduce spatial noise. Additionally, incorporate a dynamic contrastive matching strategy to eliminate background activations closely associated with the target object, ultimately producing class activation maps that are both complete and compact. Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 show that our method substantially outperforms existing weakly supervised approaches, validating the effectiveness of class-aware guidance and inter-class relational modeling in improving segmentation accuracy.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Seed perception learning for weakly supervised semantic segmentation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月15日

查看全文
  • Wanchun Sun,
  • Shujia Li,
  • Xinyu Duan

摘要

The core challenge in image-level weakly supervised semantic segmentation lies in generating high-quality object localization maps from simple image labels. Class Activation Map (CAM) produced by existing methods commonly suffer from two major flaws: incomplete coverage of target regions and severe background interference. To address these issues, we present a CAM-native perception-optimization framework for weakly supervised semantic segmentation. First, design a CAM generation mechanism guided by image-level weak supervision, which refines activated regions via discriminative region enhancement and spatial noise suppression. This process promotes fine-grained pixel clustering and improves the completeness of object localization. Second, introduce a spatial cue generator to enhance the adaptability of class representations, coupled with an inter-class relation propagation module that explicitly models inter-class relationships to suppress erroneous activations and significantly reduce spatial noise. Additionally, incorporate a dynamic contrastive matching strategy to eliminate background activations closely associated with the target object, ultimately producing class activation maps that are both complete and compact. Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 show that our method substantially outperforms existing weakly supervised approaches, validating the effectiveness of class-aware guidance and inter-class relational modeling in improving segmentation accuracy.