mlsanityData to 14 Oct 2026

Part-based Quantitative Analysis for Heatmaps

arXiv 2405.13264 ↗First posted 22 May 2024cs.LG cs.AI cs.CVcited by 0

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Abstract (arXiv)

Heatmaps have been instrumental in helping understand deep network decisions, and are a common approach for Explainable AI (XAI). While significant progress has been made in enhancing the informativeness and accessibility of heatmaps, heatmap analysis is typically very subjective and limited to domain experts. As such, developing automatic, scalable, and numerical analysis methods to make heatmap-based XAI more objective, end-user friendly, and cost-effective is vital. In addition, there is a need for comprehensive evaluation metrics to assess heatmap quality at a granular level.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 32 of its 35 references that are in the map. First posted on arXiv 22 May 2024.

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32 of 35 references.

References · 32

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  4. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
  5. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  6. 2017Axiomatic Attribution for Deep Networkscited by 3,384
  7. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,884
  8. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,546
  9. 2015Learning Deep Features for Discriminative Localizationcited by 2,436
  10. 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,371
  11. 2017Improved Regularization of Convolutional Neural Networks with Cutoutcited by 1,628
  12. 2017Random Erasing Data Augmentationcited by 1,239
20 more
  1. 2017SmoothGrad: removing noise by adding noisecited by 1,109
  2. 2014Striving for Simplicity: The All Convolutional Netcited by 990
  3. 2017Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networkscited by 939
  4. 2018RISE: Randomized Input Sampling for Explanation of Black-box Modelscited by 838
  5. 2017Interpretable Explanations of Black Boxes by Meaningful Perturbationcited by 612
  6. 2019Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networkscited by 582
  7. 2015Evaluating the visualization of what a Deep Neural Network has learnedcited by 419
  8. 2016Top-down Neural Attention by Excitation Backpropcited by 321
  9. 2017Real Time Image Saliency for Black Box Classifierscited by 246
  10. 2014Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Partscited by 232
  11. 2023Semantic-SAM: Segment and Recognize Anything at Any Granularitycited by 185
  12. 2021PartImageNet: A Large, High-Quality Dataset of Partscited by 107
  13. 2021The effectiveness of feature attribution methods and its correlation with automatic evaluation scorescited by 88
  14. 2022"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interactioncited by 63
  15. 2017Towards better understanding of gradient-based attribution methods for Deep Neural Networkscited by 54
  16. 2023Going Denser with Open-Vocabulary Part Segmentationcited by 51
  17. 2021Puzzle-CAM: Improved localization via matching partial and full featurescited by 37
  18. 2022Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methodscited by 13
  19. 2022SESS: Saliency Enhancing with Scaling and Slidingcited by 5
  20. 2023Towards Self-Explainability of Deep Neural Networks with Heatmap Captioning and Large-Language Modelscited by 3

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