mlsanityData to 7 Oct 2026

G-CAME: Gaussian-Class Activation Mapping Explainer for Object Detectors

arXiv 2306.03400 ↗First posted 6 Jun 2023cs.CV cs.AI cs.LGcited by 7

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Abstract (arXiv)

Nowadays, deep neural networks for object detection in images are very prevalent. However, due to the complexity of these networks, users find it hard to understand why these objects are detected by models. We proposed Gaussian Class Activation Mapping Explainer (G-CAME), which generates a saliency map as the explanation for object detection models. G-CAME can be considered a CAM-based method that uses the activation maps of selected layers combined with the Gaussian kernel to highlight the important regions in the image for the predicted box. Compared with other Region-based methods, G-CAME can transcend time constraints as it takes a very short time to explain an object. We also evaluated our method qualitatively and quantitatively with YOLOX on the MS-COCO 2017 dataset and guided to apply G-CAME into the two-stage Faster-RCNN model.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 25 of its 34 references that are in the map. First posted on arXiv 6 Jun 2023.

Builds on

25 of 34 references.

References · 25

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  3. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  4. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  5. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  6. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  7. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  8. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  9. 2018YOLOv3: An Incremental Improvementcited by 2,652
  10. 2015Learning Deep Features for Discriminative Localizationcited by 2,430
  11. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
  12. 2018Sanity Checks for Saliency Mapscited by 1,149
13 more
  1. 2021YOLOX: Exceeding YOLO Series in 2021cited by 1,064
  2. 2017Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networkscited by 936
  3. 2018RISE: Randomized Input Sampling for Explanation of Black-box Modelscited by 825
  4. 2017Focal Loss for Dense Object Detectioncited by 749
  5. 2019Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networkscited by 575
  6. 2016R-FCN: Object Detection via Region-based Fully Convolutional Networkscited by 414
  7. 2016Top-down Neural Attention by Excitation Backpropcited by 318
  8. 2021TOOD: Task-aligned One-stage Object Detectioncited by 218
  9. 2020Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNscited by 151
  10. 2019XRAI: Better Attributions Through Regionscited by 116
  11. 2020Black-box Explanation of Object Detectors via Saliency Mapscited by 74
  12. 2021Group-CAM: Group Score-Weighted Visual Explanations for Deep Convolutional Networkscited by 36
  13. 2021PAFNet: An Efficient Anchor-Free Object Detector Guidancecited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 0; 2026: 3.

7 papers.

  1. 2024LangXAI: Integrating Large Vision Models for Generating Textual Explanations to Enhance Explainability in Visual Perception Taskscited by 12
  2. 2024FFAM: Feature Factorization Activation Map for Explanation of 3D Detectorscited by 2
  3. 2024Enhancing the Fairness and Performance of Edge Cameras with Explainable AIcited by 1
  4. 2024XAI-Enhanced Semantic Segmentation Models for Visual Quality Inspectioncited by 1
  5. 2026GeoSEAN: Explainable Country-Level Image Geolocation for ASEAN Regionscited by 0
  6. 2026Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformationscited by 0
  7. 2026Reconstructed holograms and explanation-aware evaluation for low-cost computational pollen analysis in veterinary cytologycited by 0