Region-CAM: Towards Accurate Object Regions in Class Activation Maps for Weakly Supervised Learning Tasks

2025-10-28

AI Summary

Traditional Class Activation Mapping (CAM) activates only the most discriminative regions of the target object, resulting in incomplete coverage and poorly defined boundaries—severely limiting performance in pixel-level weakly supervised semantic segmentation (WSSS). To address this, we propose Region-CAM, the first method to introduce Semantic Information Propagation (SIP), which fuses multi-layer features and gradient signals to construct a Semantic Information Map (SIM). Region-CAM further enhances activation map completeness and boundary alignment accuracy via cross-stage semantic propagation and aggregation. The approach requires no additional supervision and is fully compatible with standard classification backbones. On PASCAL VOC 2012, Region-CAM achieves 60.12% mIoU—outperforming baseline CAM by 13.61 percentage points. On MS COCO, it improves mIoU by 16.23% and surpasses LayerCAM by 4.5% in localization accuracy (Loc1).

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Semi-Supervised Learning
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Semantics and Knowledge: Representation, semantic annotation, enhancement, enrichments, access and/or integration of a variety of data on the Web
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Class Activation Mapping (CAM) methods are widely applied in weakly supervised learning tasks due to their ability to highlight object regions. However, conventional CAM methods highlight only the most discriminative regions of the target. These highlighted regions often fail to cover the entire object and are frequently misaligned with object boundaries, thereby limiting the performance of downstream weakly supervised learning tasks, particularly Weakly Supervised Semantic Segmentation (WSSS), which demands pixel-wise accurate activation maps to get the best results. To alleviate the above problems, we propose a novel activation method, Region-CAM. Distinct from network feature weighting approaches, Region-CAM generates activation maps by extracting semantic information maps (SIMs) and performing semantic information propagation (SIP) by considering both gradients and features in each of the stages of the baseline classification model. Our approach highlights a greater proportion of object regions while ensuring activation maps to have precise boundaries that align closely with object edges. Region-CAM achieves 60.12% and 58.43% mean intersection over union (mIoU) using the baseline model on the PASCAL VOC training and validation datasets, respectively, which are improvements of 13.61% and 13.13% over the original CAM (46.51% and 45.30%). On the MS COCO validation set, Region-CAM achieves 36.38%, a 16.23% improvement over the original CAM (20.15%). We also demonstrate the superiority of Region-CAM in object localization tasks, using the ILSVRC2012 validation set. Region-CAM achieves 51.7% in Top-1 Localization accuracy Loc1. Compared with LayerCAM, an activation method designed for weakly supervised object localization, Region-CAM achieves 4.5% better performance in Loc1.

Problem

• CAM methods highlight only discriminative object regions inadequately
• Conventional CAM fails to cover full objects with precise boundaries
• Proposes Region-CAM to generate complete object regions with accurate edges

Innovation

• Generates activation maps via semantic information maps
• Propagates semantic information using gradients and features
• Produces precise object boundaries for accurate localization