ConEx: Human-Interpretable Saliency Maps via Concept-Aware Attribution
cs.CV, cs.AI, cs.LG
Submitted: 2026-10-03
Updated: 2026-10-03
Code: https://github.com/yonisGit/conex
Sources
- Visual-TCAV: Concept-based Attribution and Saliency Maps for Post-hoc Explainability in Image Classification
- Towards A Rigorous Science of Interpretable Machine Learning
- XCiT: Cross-Covariance Image Transformers
- Attention is not Explanation
- Missingness Bias in Model Debugging
- DINOv2: Learning Robust Visual Features without Supervision
- Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks
- Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
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