mlsanityData to 7 Oct 2026

IS-CAM: Integrated Score-CAM for axiomatic-based explanations

arXiv 2010.03023 ↗First posted 6 Oct 2020cs.CVcited by 29

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Abstract (arXiv)

Convolutional Neural Networks have been known as black-box models as humans cannot interpret their inner functionalities. With an attempt to make CNNs more interpretable and trustworthy, we propose IS-CAM (Integrated Score-CAM), where we introduce the integration operation within the Score-CAM pipeline to achieve visually sharper attribution maps quantitatively. Our method is evaluated on 2000 randomly selected images from the ILSVRC 2012 Validation dataset, which proves the versatility of IS-CAM to account for different models and methods.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Oct 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 8; 2023: 6; 2024: 6; 2025: 5; 2026: 1.

29 papers.

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  1. 2021F-CAM: Full Resolution Class Activation Maps via Guided Parametric Upscalingcited by 26
  2. 2021Revisiting The Evaluation of Class Activation Mapping for Explainability: A Novel Metric and Experimental Analysiscited by 25
  3. 2022Discriminative Sampling of Proposals in Self-Supervised Transformers for Weakly Supervised Object Localizationcited by 13
  4. 2022TCAM: Temporal Class Activation Maps for Object Localization in Weakly-Labeled Unconstrained Videoscited by 12
  5. 2022Re-Attention Transformer for Weakly Supervised Object Localizationcited by 11
  6. 2024Guided Interpretable Facial Expression Recognition via Spatial Action Unit Cuescited by 11
  7. 2022Poly-CAM: High resolution class activation map for convolutional neural networkscited by 10
  8. 2023Explain To Me: Salience-Based Explainability for Synthetic Face Detection Modelscited by 8
  9. 2023DiPS: Discriminative Pseudo-Label Sampling with Self-Supervised Transformers for Weakly Supervised Object Localizationcited by 8
  10. 2023CoLo-CAM: Class Activation Mapping for Object Co-Localization in Weakly-Labeled Unconstrained Videoscited by 7
  11. 2022TSEM: Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time Seriescited by 4
  12. 2023ViT-ReciproCAM: Gradient and Attention-Free Visual Explanations for Vision Transformercited by 4
17 more
  1. 2025From Visual Explanations to Counterfactual Explanations with Latent Diffusioncited by 4
  2. 2022Recipro-CAM: Fast gradient-free visual explanations for convolutional neural networkscited by 3
  3. 2022Crown-CAM: Interpretable Visual Explanations for Tree Crown Detection in Aerial Imagescited by 2
  4. 2023Overview of Class Activation Maps for Visualization Explainabilitycited by 2
  5. 2024Challenging the Black Box: A Comprehensive Evaluation of Attribution Maps of CNN Applications in Agriculture and Forestrycited by 2
  6. 2024Leveraging Transformers for Weakly Supervised Object Localization in Unconstrained Videoscited by 2
  7. 2025AEGIS: Human Attention-based Explainable Guidance for Intelligent Vehicle Systemscited by 2
  8. 2021Weakly Supervised Visual-Auditory Fixation Prediction with Multigranularity Perceptioncited by 1
  9. 2023BroadCAM: Outcome-agnostic Class Activation Mapping for Small-scale Weakly Supervised Applicationscited by 1
  10. 2024MetaDD: Boosting Dataset Distillation with Neural Network Architecture-Invariant Generalizationcited by 1
  11. 2025Explainable Artificial Intelligence in Biomedical Image Analysis: A Comprehensive Surveycited by 1
  12. 2022Conceptor Learning for Class Activation Mappingcited by 0
  13. 2024Tensor Polynomial Additive Modelcited by 0
  14. 2024Integrated feature analysis for deep learning interpretation and class activation mapscited by 0
  15. 2025CF-CAM: Cluster Filter Class Activation Mapping for Reliable Gradient-Based Interpretabilitycited by 0
  16. 2025CAMBench-QR : A Structure-Aware Benchmark for Post-Hoc Explanations with QR Understandingcited by 0
  17. 2026How Can One Choose the Best CAM-Based Explainability Method for a CNN Model?cited by 0