mlsanityData to 14 Oct 2026

Efficient and Concise Explanations for Object Detection with Gaussian-Class Activation Mapping Explainer

arXiv 2404.13417 ↗First posted 20 Apr 2024cs.CV cs.AIcited by 6

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Abstract (arXiv)

To address the challenges of providing quick and plausible explanations in Explainable AI (XAI) for object detection models, we introduce the Gaussian Class Activation Mapping Explainer (G-CAME). Our method efficiently generates concise saliency maps by utilizing activation maps from selected layers and applying a Gaussian kernel to emphasize critical image regions for the predicted object. Compared with other Region-based approaches, G-CAME significantly reduces explanation time to 0.5 seconds without compromising the quality. Our evaluation of G-CAME, using Faster-RCNN and YOLOX on the MS-COCO 2017 dataset, demonstrates its ability to offer highly plausible and faithful explanations, especially in reducing the bias on tiny object detection.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 23 of its 30 references that are in the map. First posted on arXiv 20 Apr 2024.

Builds on

23 of 30 references.

References · 23

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  2. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  3. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
  4. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  5. 2016Feature Pyramid Networks for Object Detectioncited by 5,036
  6. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  7. 2015SSD: Single Shot MultiBox Detectorcited by 3,130
  8. 2015Learning Deep Features for Discriminative Localizationcited by 2,436
  9. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,162
  10. 2018Sanity Checks for Saliency Mapscited by 1,156
  11. 2021YOLOX: Exceeding YOLO Series in 2021cited by 1,071
  12. 2017Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networkscited by 939
11 more
  1. 2018RISE: Randomized Input Sampling for Explanation of Black-box Modelscited by 838
  2. 2019Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networkscited by 582
  3. 2016R-FCN: Object Detection via Region-based Fully Convolutional Networkscited by 414
  4. 2016Top-down Neural Attention by Excitation Backpropcited by 321
  5. 2020Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNscited by 151
  6. 2019XRAI: Better Attributions Through Regionscited by 116
  7. 2020Black-box Explanation of Object Detectors via Saliency Mapscited by 76
  8. 2021Group-CAM: Group Score-Weighted Visual Explanations for Deep Convolutional Networkscited by 36
  9. 2023Towards Better Explanations for Object Detectioncited by 14
  10. 2023Towards Trust of Explainable AI in Thyroid Nodule Diagnosiscited by 10
  11. 2021PAFNet: An Efficient Anchor-Free Object Detector Guidancecited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 2; 2026: 1.

6 papers.

  1. 2024XEdgeAI: A Human-centered Industrial Inspection Framework with Data-centric Explainable Edge AI Approachcited by 8
  2. 2025Heart2Mind: Human-Centered Contestable Psychiatric Disorder Diagnosis System using Wearable ECG Monitorscited by 4
  3. 2025ODExAI: A Comprehensive Object Detection Explainable AI Evaluationcited by 3
  4. 2024MACeIP: A Multimodal Ambient Context-enriched Intelligence Platform in Smart Citiescited by 2
  5. 2024On the Black-box Explainability of Object Detection Models for Safe and Trustworthy Industrial Applicationscited by 2
  6. 2026SAM-Sode: Towards Faithful Explanations for Tiny Bacteria Detectioncited by 0