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

arXiv: https://arxiv.org/abs/2010.03023 · HTML: https://mlsanity.com/paper/2010.03023/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2020.

## Built on by (most cited first, 29 of 29)

- [F-CAM: Full Resolution Class Activation Maps via Guided Parametric Upscaling](https://mlsanity.com/paper/2109.07069/) (2021)
- [Revisiting The Evaluation of Class Activation Mapping for Explainability: A Novel Metric and Experimental Analysis](https://mlsanity.com/paper/2104.10252/) (2021)
- [Discriminative Sampling of Proposals in Self-Supervised Transformers for Weakly Supervised Object Localization](https://mlsanity.com/paper/2209.09209/) (2022)
- [TCAM: Temporal Class Activation Maps for Object Localization in Weakly-Labeled Unconstrained Videos](https://mlsanity.com/paper/2208.14542/) (2022)
- [Re-Attention Transformer for Weakly Supervised Object Localization](https://mlsanity.com/paper/2208.01838/) (2022)
- [Guided Interpretable Facial Expression Recognition via Spatial Action Unit Cues](https://mlsanity.com/paper/2402.00281/) (2024)
- [Poly-CAM: High resolution class activation map for convolutional neural networks](https://mlsanity.com/paper/2204.13359/) (2022)
- [Explain To Me: Salience-Based Explainability for Synthetic Face Detection Models](https://mlsanity.com/paper/2303.11969/) (2023)
- [DiPS: Discriminative Pseudo-Label Sampling with Self-Supervised Transformers for Weakly Supervised Object Localization](https://mlsanity.com/paper/2310.06196/) (2023)
- [CoLo-CAM: Class Activation Mapping for Object Co-Localization in Weakly-Labeled Unconstrained Videos](https://mlsanity.com/paper/2303.09044/) (2023)
- [TSEM: Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time Series](https://mlsanity.com/paper/2205.13012/) (2022)
- [ViT-ReciproCAM: Gradient and Attention-Free Visual Explanations for Vision Transformer](https://mlsanity.com/paper/2310.02588/) (2023)
- [From Visual Explanations to Counterfactual Explanations with Latent Diffusion](https://mlsanity.com/paper/2504.09202/) (2025)
- [Recipro-CAM: Fast gradient-free visual explanations for convolutional neural networks](https://mlsanity.com/paper/2209.14074/) (2022)
- [Crown-CAM: Interpretable Visual Explanations for Tree Crown Detection in Aerial Images](https://mlsanity.com/paper/2211.13126/) (2022)
- [Overview of Class Activation Maps for Visualization Explainability](https://mlsanity.com/paper/2309.14304/) (2023)
- [Challenging the Black Box: A Comprehensive Evaluation of Attribution Maps of CNN Applications in Agriculture and Forestry](https://mlsanity.com/paper/2402.11670/) (2024)
- [Leveraging Transformers for Weakly Supervised Object Localization in Unconstrained Videos](https://mlsanity.com/paper/2407.06018/) (2024)
- [AEGIS: Human Attention-based Explainable Guidance for Intelligent Vehicle Systems](https://mlsanity.com/paper/2504.05950/) (2025)
- [Weakly Supervised Visual-Auditory Fixation Prediction with Multigranularity Perception](https://mlsanity.com/paper/2112.13697/) (2021)
- [BroadCAM: Outcome-agnostic Class Activation Mapping for Small-scale Weakly Supervised Applications](https://mlsanity.com/paper/2309.03509/) (2023)
- [MetaDD: Boosting Dataset Distillation with Neural Network Architecture-Invariant Generalization](https://mlsanity.com/paper/2410.05103/) (2024)
- [Explainable Artificial Intelligence in Biomedical Image Analysis: A Comprehensive Survey](https://mlsanity.com/paper/2507.07148/) (2025)
- [Conceptor Learning for Class Activation Mapping](https://mlsanity.com/paper/2201.08636/) (2022)
- [Tensor Polynomial Additive Model](https://mlsanity.com/paper/2406.02980/) (2024)
- [Integrated feature analysis for deep learning interpretation and class activation maps](https://mlsanity.com/paper/2407.01142/) (2024)
- [CF-CAM: Cluster Filter Class Activation Mapping for Reliable Gradient-Based Interpretability](https://mlsanity.com/paper/2504.00060/) (2025)
- [CAMBench-QR : A Structure-Aware Benchmark for Post-Hoc Explanations with QR Understanding](https://mlsanity.com/paper/2509.16745/) (2025)
- [How Can One Choose the Best CAM-Based Explainability Method for a CNN Model?](https://mlsanity.com/paper/2605.02007/) (2026)

## 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.