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arXiv · 2509.12496

Localized Region Guidance for Class Activation Mapping in WSSS

Ali Torabi·Sanjog Gaihre·MD Mahbubur Rahman·Yaqoob Majeed
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Abstract

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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Ali Torabi, Sanjog Gaihre, MD Mahbubur Rahman, Yaqoob Majeed. 2025-09-15. Localized Region Guidance for Class Activation Mapping in WSSS. https://arxiv.org/abs/2509.12496

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