Class Activation Mapping in Explainable Computer Vision: A Method-Centered Review of CNN, Transformer, and Foundation-Model-Era Visual Explanations
Class activation mapping is a widely used method in explainable AI for visual explanations, converting model evidence into heatmaps that highlight image regions.
- Class activation mapping is a widely used method in explainable AI for visual explanations.
- CAM has evolved beyond global-average-pooled CNN classifiers to include gradient-based and gradient-free methods.
- High-resolution upscaling and weakly supervised localization are now part of CAM-style methods.
Class activation mapping (CAM) is a popular method in explainable artificial intelligence for visual explanations. It converts internal model evidence into heatmaps that highlight image regions, convolutional channels, tokens, or patches that support a target class or concept. Since its first formulation in 2016, the field has moved beyond global-average-pooled CNN classifiers. Today, CAM-style methods include gradient-based post-hoc explanations, gradient-free score and ablation methods, high-resolution upscaling, and weakly supervised localization. This review provides a comprehensive overview of the evolution of CAM in explainable AI.
Understanding CAM can help developers create more transparent and explainable AI models.
Explainable AI is crucial for businesses to build trust with customers and stakeholders.
Investors need to understand the importance of explainability in AI for informed decision-making.
Learning about CAM can help students understand the basics of explainable AI and its applications.
Explainable AI is essential for building trust in AI systems and their decision-making processes.
- Class Activation Mapping
- A method in explainable AI that converts internal model evidence into heatmaps highlighting image regions.
- Explainable AI
- A subfield of AI that focuses on making AI decision-making processes transparent and understandable.
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