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Keyword

visual explanations

Description: Visual explanations communicate reasons, answers, or system behavior through visual information, including in human-computer interaction and artificial intelligence.
OpenAlex ID: keywords/visual-explanations
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
2017 · Ramprasaath R. Selvaraju, Michael Cogswell, et al. · IEEE International Conference on Computer Vision (ICCV) · 24,358
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
2019 · Ramprasaath R. Selvaraju, Michael Cogswell, et al. · International Journal of Computer Vision · 6,858
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Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks
2018 · Aditya Chattopadhay, Anirban Sarkar, et al. · IEEE Winter Conference on Applications of Computer Vision · 3,611
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Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books
2015 · Yukun Zhu, Ryan Kiros, et al. · IEEE International Conference on Computer Vision (ICCV) · 2,263
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Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks
2020 · Haofan Wang, Zifan Wang, et al. · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · 1,503
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Visual Explanations: Images and Quantities, Evidence and Narrative
1998 · Edward R. Tufte, Susan R. McKay, et al. · Computers in Physics · 897
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Analyzing Federated Learning through an Adversarial Lens
2018 · Bhagoji, Arjun Nitin, Chakraborty, Supriyo, et al. · arXiv (Cornell University) · 695
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Visual Explanations
1997 · Edward R. Tufte · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · 683
Tell Me Where to Look: Guided Attention Inference Network
2018 · Kunpeng Li, Ziyan Wu, et al. · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 550
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Attention Branch Network: Learning of Attention Mechanism for Visual Explanation
2019 · Hiroshi Fukui, Tsubasa Hirakawa, et al. · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 542
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Part-Stacked CNN for Fine-Grained Visual Categorization
2016 · Shaoli Huang, Zhe Xu, et al. · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 483
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MGraphDTA: deep multiscale graph neural network for explainable drug–target binding affinity prediction
2022 · Ziduo Yang, Weihe Zhong, et al. · Chemical Science · 347
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Grad-CAM: Why did you say that?
2016 · Selvaraju, Ramprasaath R, Das, Abhishek, et al. · arXiv (Cornell University) · 343
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CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization
2020 · Zijie J. Wang, Robert Turko, et al. · IEEE Transactions on Visualization and Computer Graphics · 327
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Visual Explanations: Images and Quantities, Evidence and Narrative
1998 · Douglas Herrmann, Edward R. Tufte · Journal of the American Statistical Association · 327
Counterfactual Visual Explanations
2019 · Goyal, Yash, Wu, Ziyan, et al. · arXiv (Cornell University) · 322
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A Systematic Review of Explainable Artificial Intelligence in Terms of Different Application Domains and Tasks
2022 · Mir Riyanul Islam, Mobyen Uddin Ahmed, et al. · Applied Sciences · 313
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Personalized Fashion Recommendation with Visual Explanations based on Multimodal Attention Network
2019 · Xu Chen, Hanxiong Chen, et al. · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · 310
Creating visual explanations improves learning
2016 · Eliza Bobek, Barbara Tversky · Cognitive Research Principles and Implications · 298
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Towards Visually Explaining Variational Autoencoders
2020 · Wenqian Liu, Runze Li, et al. · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 294
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Unlocking the black box: an in-depth review on interpretability, explainability, and reliability in deep learning
2024 · Emrullah ŞAHiN, Naciye Nur Arslan, et al. · Neural Computing and Applications · 284
Explainable artificial intelligence for breast cancer: A visual case-based reasoning approach
2019 · Jean-Baptiste Lamy, Boomadevi Sekar, et al. · Artificial Intelligence in Medicine · 281
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Interpretable Basis Decomposition for Visual Explanation
2018 · Bolei Zhou, Yiyou Sun, et al. · Lecture notes in computer science · 269
Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges
2018 · Gabriëlle Ras, Marcel van Gerven, et al. · ˜The œSpringer series on challenges in machine learning · 255
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Grad-CAM: Why did you say that? Visual Explanations from Deep Networks via Gradient-based Localization
2016 · Ramprasaath R. Selvaraju, Abhishek Das, et al. · arXiv (Cornell University) · 196
Works count: 1,568
Citation count: 60,840
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