Fusion-CAM: Integrating Gradient and Region-Based Class Activation Maps for Robust Visual Explanations
Fusion-CAM: Integrating Gradient and Region-Based Class Activation Maps for Robust Visual Explanations explores Fusion-CAM enhances the interpretability of deep learning models by adaptively fusing gradient and region-based Class Activation Maps, offering a more robust and detailed visual explanation.. Commercial viability score: 7/10 in Explainable AI.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisCombi-CAM: A Novel Multi-Layer Approach for Explainable Image GeolocalizationViability: 3.0
- Builds On ThisWhat Helps -- and What Hurts: Bidirectional Explanations for Vision TransformersViability: 3.0
- Builds On ThisExplainable AI: A Combined XAI Framework for Explaining Brain Tumour Detection ModelsViability: 5.0
- Builds On ThisPost-hoc Self-explanation of CNNsViability: 4.0
- Builds On ThisEnd-to-end Feature Alignment: A Simple CNN with Intrinsic Class AttributionViability: 5.0