# SS-CAM: Smoothed Score-CAM for Sharper Visual Feature Localization

arXiv: https://arxiv.org/abs/2006.14255 · HTML: https://mlsanity.com/paper/2006.14255/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2020.

## Built on by (most cited first, 30 of 46)

- [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)
- [Opti-CAM: Optimizing saliency maps for interpretability](https://mlsanity.com/paper/2301.07002/) (2023)
- [What Makes a Good Explanation?: A Harmonized View of Properties of Explanations](https://mlsanity.com/paper/2211.05667/) (2022)
- [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)
- [ODAM: Gradient-based instance-specific visual explanations for object detection](https://mlsanity.com/paper/2304.06354/) (2023)
- [Explainable Artificial Intelligence for Medical Applications: A Review](https://mlsanity.com/paper/2412.01829/) (2024)
- [Re-Attention Transformer for Weakly Supervised Object Localization](https://mlsanity.com/paper/2208.01838/) (2022)
- [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)
- [Grad-ECLIP: Gradient-based Visual and Textual Explanations for CLIP](https://mlsanity.com/paper/2502.18816/) (2025)
- [CoLo-CAM: Class Activation Mapping for Object Co-Localization in Weakly-Labeled Unconstrained Videos](https://mlsanity.com/paper/2303.09044/) (2023)
- [Model Doctor: A Simple Gradient Aggregation Strategy for Diagnosing and Treating CNN Classifiers](https://mlsanity.com/paper/2112.04934/) (2021)
- [An Explainable Non-local Network for COVID-19 Diagnosis](https://mlsanity.com/paper/2408.04300/) (2024)
- [SSA: Semantic Structure Aware Inference for Weakly Pixel-Wise Dense Predictions without Cost](https://mlsanity.com/paper/2111.03392/) (2021)
- [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)
- [Enhancing the "Immunity" of Mixture-of-Experts Networks for Adversarial Defense](https://mlsanity.com/paper/2402.18787/) (2024)
- [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)
- [Exploring Hyperspectral Anomaly Detection with Human Vision: A Small Target Aware Detector](https://mlsanity.com/paper/2401.01093/) (2024)
- [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)
- [AdaptGrad: Adaptive Sampling to Reduce Noise](https://mlsanity.com/paper/2410.07711/) (2024)
- [Weakly Supervised Visual-Auditory Fixation Prediction with Multigranularity Perception](https://mlsanity.com/paper/2112.13697/) (2021)
- [Model LEGO: Creating Models Like Disassembling and Assembling Building Blocks](https://mlsanity.com/paper/2203.13453/) (2022)

## Abstract (arXiv)

Interpretation of the underlying mechanisms of Deep Convolutional Neural Networks has become an important aspect of research in the field of deep learning due to their applications in high-risk environments. To explain these black-box architectures there have been many methods applied so the internal decisions can be analyzed and understood. In this paper, built on the top of Score-CAM, we introduce an enhanced visual explanation in terms of visual sharpness called SS-CAM, which produces centralized localization of object features within an image through a smooth operation. We evaluate our method on the ILSVRC 2012 Validation dataset, which outperforms Score-CAM on both faithfulness and localization tasks.