Technology Category
Computer Vision: SegmentationMachine Learning: Semi-Supervised LearningConstraint Satisfaction and Optimization: Constraint Satisfaction
Instance-Guided Class Activation Mapping for Weakly Supervised Semantic Segmentation
🤖 AI Summary
Weakly supervised semantic segmentation (WSSS) suffers from incomplete object localization, blurred boundaries, and bias toward discriminative local regions due to reliance solely on image-level labels. To address these limitations, this paper proposes an instance-guided influence function modeling framework with three key innovations: (1) leveraging instance-level cues to guide Class Activation Map (CAM) generation, thereby improving object completeness; (2) modeling pixel-wise contributions to classification decisions via influence functions to enhance boundary sensitivity; and (3) integrating multi-scale progressive optimization with Conditional Random Field (CRF) post-processing for fine-grained structural recovery. Evaluated on PASCAL VOC 2012, the method achieves 82.3% mean Intersection-over-Union (mIoU), rising to 86.6% after CRF refinement—outperforming state-of-the-art WSSS approaches. Notably, it delivers substantial improvements in object completeness and boundary delineation.
Technology Category
Application Category
📝 Abstract
Weakly Supervised Semantic Segmentation (WSSS) addresses the challenge of training segmentation models using only image-level annotations, eliminating the need for expensive pixel-level labeling. While existing methods struggle with precise object boundary localization and often focus only on the most discriminative regions, 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 that uses ground truth segmentation masks to guide CAM generation, ensuring complete object coverage rather than just discriminative parts; (2) Influence Function Integration that captures the relationship between training samples and model predictions, leading to more robust feature representations; and (3) Multi-Scale Boundary Enhancement that employs progressive refinement strategies to achieve sharp, precise object boundaries. IG-CAM achieves state-of-the-art performance on the PASCAL VOC 2012 dataset with an mIoU of 82.3% before post-processing, which further improves to 86.6% after applying Conditional Random Field (CRF) refinement, significantly outperforming previous WSSS methods. Our approach demonstrates superior localization accuracy, with complete object coverage and precise boundary delineation, while maintaining computational efficiency. Extensive ablation studies validate the contribution of each component, and qualitative comparisons across 600 diverse images showcase the method's robustness and generalization capability. The results establish IG-CAM as a new benchmark for weakly supervised semantic segmentation, offering a practical solution for scenarios where pixel-level annotations are unavailable or prohibitively expensive.
Problem
Research questions and friction points this paper is trying to address.
Generating precise object boundaries using only image-level annotations
Overcoming focus on discriminative regions rather than complete objects
Improving localization maps without expensive pixel-level supervision
Innovation
Methods, ideas, or system contributions that make the work stand out.
Instance-Guided Refinement using ground truth masks
Influence Function Integration for robust features
Multi-Scale Boundary Enhancement for precise edges
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