用实例提示生成更准的分割图，边界更清晰。
Localized Region Guidance for Class Activation Mapping in WSSS
- 用目标建议框引导激活图生成，覆盖完整物体。
- 引入影响函数捕捉训练样本与预测关系，提升定位精度。
- 多尺度边界增强策略，适合弱监督分割研究者。
弱监督语义分割（WSSS）仅使用图像级标注训练分割模型，但现有方法在精确定位物体边界方面表现不佳，且仅关注最显著区域。为此，本文提出IG-CAM（实例引导类激活映射），利用实例级线索和影响函数生成高质量、边界感知的定位图。该方法包含三项创新：(1) 实例引导精炼，通过对象建议框指导CAM生成，确保物体完整覆盖；(2) 影响函数融合，捕捉训练样本与模型预测间的关系；(3) 多尺度边界增强，采用渐进式精炼策略。在PASCAL VOC 2012上，IG-CAM未后处理时达到82.3% mIoU，经CRF优化后提升至86.6%，显著优于以往WSSS方法。大量消融实验验证了各组件的有效性，确立了IG-CAM作为弱监督分割新基准。
原文摘要 · Abstract (English)
Weakly Supervised Semantic Segmentation (WSSS) addresses the challenge of training segmentation models using only image-level annotations. Existing WSSS methods struggle with precise object boundary localization and focus only on the most discriminative regions. To address these challenges, we propose IG-CAM (Instance-Guided Class Activation Mapping), a novel approach that leverages instance-level cues and influence functions to generate high-quality, boundary-aware localization maps. Our method introduces three key innovations: (1) Instance-Guided Refinement using object proposals to guide CAM generation, ensuring complete object coverage; (2) Influence Function Integration that captures the relationship between training samples and model predictions; and (3) Multi-Scale Boundary Enhancement with progressive refinement strategies. IG-CAM achieves state-of-the-art performance on PASCAL VOC 2012 with 82.3% mIoU before post-processing, improving to 86.6% after CRF refinement, significantly outperforming previous WSSS methods. Extensive ablation studies validate each component's contribution, establishing IG-CAM as a new benchmark for weakly supervised semantic segmentation.
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