ArticleProceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition2023
CRAFT: Concept Recursive Activation FacTorization for Explainability.
Article in Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
6 citing papers in PubMed.
- Article
- Lightweight and interpretable edge intelligence AI with intrusion detection for trustworthy cardiac arrhythmia in medical IoT.Scientific reports · 2026Article
- Face2Bone explainable AI model predicts osteoporosis risk from facial images in proof of concept study.Scientific reports · 2025Observational
- Ensuring medical AI safety: interpretability-driven detection and mitigation of spurious model behavior and associated data.Machine learning · 2025Article
- Interpretability Is in the Mind of the Beholder: A Causal Framework for Human-Interpretable Representation Learning.Entropy (Basel, Switzerland) · 2023Article
- CRAFT: Concept Recursive Activation FacTorization for Explainability.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Attribution methods, which employ heatmaps to identify the most influential regions of an image that impact model decisions, have gained widespread popularity as a type of explainability method. However, recent research has exposed the limited practical value of these methods, attributed in part to their narrow focus on the most prominent regions of an image - revealing "where" the model looks, but failing to elucidate "what" the model sees in those areas. In this work, we try to fill in this gap with CRAFT - a novel approach to identify both "what" and "where" by generating concept-based explanations. We introduce 3 new ingredients to the automatic concept extraction literature: (
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.