Grounding visual explanations

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works

Fast R-CNN

Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105 · see citing works

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555 · see citing works

Visualizing and understanding convolutional networks

Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839 · see citing works

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248 · see citing works

Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning

Williams, R. J. (1992) · Machine Learning · doi:10.1007/bf00992696 · Cited by 7,936 · see citing works

“Why Should I Trust You?”: Explaining the Predictions of Any Classifier

Ribeiro, M., Singh, S., & Guestrin, C. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations · doi:10.18653/v1/n16-3020 · Cited by 6,682 · see citing works

Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations

Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.-J., Shamma, D. A., et al. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-016-0981-7 · Cited by 5,107 · see citing works

Long-Term Recurrent Convolutional Networks for Visual Recognition and Description

Donahue, J., Hendricks, L. A., Rohrbach, M., Venugopalan, S., Guadarrama, S., Saenko, K., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2599174 · Cited by 1,547 · see citing works

A model of inexact reasoning in medicine

Shortliffe, E. H., & Buchanan, B. G. (1975) · Mathematical Biosciences · doi:10.1016/0025-5564(75)90047-4 · Cited by 1,447 · see citing works

Hierarchical Question-Image Co-Attention for Visual Question AnsweringPreprint

Lu, J., Yang, J., Batra, D., & Parikh, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.00061 · Cited by 1,303 · see citing works

Generation and Comprehension of Unambiguous Object Descriptions

Mao, J., Huang, J., Toshev, A., Camburu, O., Yuille, A., & Murphy, K. P. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.9 · Cited by 1,211 · see citing works

Interpretable Explanations of Black Boxes by Meaningful Perturbation

Fong, R. C., & Vedaldi, A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.371 · Cited by 1,097 · see citing works

Relative attributes

Parikh, D., & Grauman, K. (2011) · International Conference on Computer Vision · doi:10.1109/iccv.2011.6126281 · Cited by 901 · see citing works

Learning Deep Representations of Fine-Grained Visual Descriptions

Reed, S., Akata, Z., Lee, H., & Schiele, B. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.13 · Cited by 802 · see citing works

Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model

Letham, B., Rudin, C., McCormick, T. H., & Madigan, D. (2015) · The Annals of Applied Statistics · doi:10.1214/15-aoas848 · Cited by 766 · see citing works

The structure and function of explanations.

Lombrozo, T. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.08.004 · Cited by 729 · see citing works

"Why Should I Trust You?": Explaining the Predictions of Any ClassifierPreprint

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.04938 · Cited by 587 · see citing works

Generating Visual Explanations

Hendricks, L. A., Akata, Z., Rohrbach, M., Donahue, J., Schiele, B., & Darrell, T. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_1 · Cited by 534 · see citing works

Visualizing Deep Neural Network Decisions: Prediction Difference Analysis

Zintgraf, L. M., Cohen, T. S., Adel, T., & Welling, M. (2017) · arXiv · doi:10.48550/arxiv.1702.04595 · Cited by 487 · see citing works

Visual question answering: A survey of methods and datasets

Wu, Q., Teney, D., Wang, P., Shen, C., Dick, A., & van den Hengel, A. (2017) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2017.05.001 · Cited by 442 · see citing works

Multimodal Explanations: Justifying Decisions and Pointing to the Evidence

Park, D. H., Hendricks, L. A., Akata, Z., Rohrbach, A., Schiele, B., Darrell, T., & Rohrbach, M. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00915 · Cited by 418 · see citing works

Modeling Relationships in Referential Expressions with Compositional Modular Networks

Hu, R., Rohrbach, M., Andreas, J., Darrell, T., & Saenko, K. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.470 · Cited by 386 · see citing works

An analysis of physician attitudes regarding computer-based clinical consultation systems

Teach, R. L., & Shortliffe, E. H. (1981) · Computers and Biomedical Research · doi:10.1016/0010-4809(81)90012-4 · Cited by 330 · see citing works

Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image AnnotationsPreprint

Krishna, R., Zhu, Y., Groth, O., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.07332 · Cited by 248 · see citing works

Explanation and abductive inference

Lombrozo, T. (2012) · The Oxford Handbook of Thinking and Reasoning · doi:10.1093/oxfordhb/9780199734689.013.0014 · Cited by 242 · see citing works

An explainable artificial intelligence system for small-unit tactical behavior

van Lent, M., Fisher, W. M., & Mancuso, M. R. (2004) · Cited by 215 · see citing works

Learning Deep Representations of Fine-grained Visual DescriptionsPreprint

Reed, S., Akata, Z., Schiele, B., & Lee, H. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.05395 · Cited by 154 · see citing works

Building Explainable Artificial Intelligence Systems

Core, M. G., Lane, H. C., van Lent, M., Gomboc, D., Solomon, S., & Milton Rosenberg, B. (2006) · doi:10.21236/ada459166 · Cited by 134 · see citing works

Reasoning about Pragmatics with Neural Listeners and Speakers

Andreas, J., & Klein, D. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1125 · Cited by 115 · see citing works

Context-Aware Captions from Context-Agnostic Supervision

Vedantam, R., Samy Bengio, A., Murphy, K. P., Parikh, D., & Chechik, G. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.120 · Cited by 113 · see citing works

FOIL it! Find One mismatch between Image and Language caption

Shekhar, R., Pezzelle, S., Klimovich, Y., Herbelot, A., Nabi, M., Sangineto, E., & Raffaella Bernardi, B. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1024 · Cited by 111 · see citing works

Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (2013)Preprint

Nicholson, A. E., & Smyth, P. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1309.7971 · Cited by 89 · see citing works

Explainable Artificial Intelligence for Training and Tutoring

Lane, H. C., Core, M. G., van Lent, M., Solomon, S., & Gomboc, D. (2005) · Cited by 41 · see citing works

Reasoning About Pragmatics with Neural Listeners and SpeakersPreprint

Andreas, J., & Klein, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.00562 · Cited by 38 · see citing works

Evaluating computational models of explanation using human judgmentsPreprint

Pacer, M., Williams, J. J., Chen, X., Lombrozo, T., & Griffiths, T. L. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1309.6855 · Cited by 28 · see citing works

Context-aware Captions from Context-agnostic SupervisionPreprint

Vedantam, R., Bengio, S., Murphy, K. P., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.02870 · Cited by 24 · see citing works

From Recognition to Cognition: Visual Commonsense Reasoning

Zellers, R., Bisk, Y., Farhadi, A., & Choi, Y. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00688 · Cited by 769 · see citing works

Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI

Jacovi, A., Marasović, A., Miller, T., & Goldberg, Y. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445923 · Cited by 545 · see citing works

Counterfactual VQA: A Cause-Effect Look at Language Bias

Niu, Y., Tang, K., Zhang, H., Lu, Z., Hua, X., & Wen, J.-R. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01251 · Cited by 448 · see citing works

A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence

Stepin, I., Alonso, J. M., Catala, A., & Pereira-Farina, M. (2021) · IEEE Access · doi:10.1109/access.2021.3051315 · Cited by 376 · see citing works

From Show to Tell: A Survey on Deep Learning-Based Image Captioning

Stefanini, M., Cornia, M., Baraldi, L., Cascianelli, S., Fiameni, G., & Cucchiara, R. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3148210 · Cited by 373 · see citing works

Textual Explanations for Self-Driving Vehicles

Kim, J., Rohrbach, A., Darrell, T., Canny, J., & Akata, Z. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01216-8_35 · Cited by 355 · see citing works

A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts

Schwalbe, G., & Finzel, B. (2023) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-022-00867-8 · Cited by 381 · see citing works

Counterfactual Visual ExplanationsPreprint

Goyal, Y., Wu, Z., Ernst, J., Batra, D., Parikh, D., & Lee, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.07451 · Cited by 326 · see citing works

Survey on Explainable AI: From Approaches, Limitations and Applications Aspects

Yang, W., Wei, Y.-C., Wei, H., Chen, Y., Huang, G., Li, X., Li, R., Yao, N., Wang, X., Gu, X., et al. (2023) · Human-Centric Intelligent Systems · doi:10.1007/s44230-023-00038-y · Cited by 275 · see citing works

Explainability of Deep Vision-Based Autonomous Driving Systems: Review and Challenges

Zablocki, É., Ben-Younes, H., Pérez, P., & Cord, M. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01657-x · Cited by 227 · see citing works

Evaluating Weakly Supervised Object Localization Methods Right

Choe, J., Oh, S. J., Lee, S., Chun, S., Akata, Z., & Shim, H. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00320 · Cited by 204 · see citing works

Robust Change Captioning

Park, D. H., Darrell, T., & Rohrbach, A. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00472 · Cited by 201 · see citing works

Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classification

Yang, Y., Panagopoulou, A., Zhou, S., Jin, D., Callison-Burch, C., & Yatskar, M. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01839 · Cited by 162 · see citing works

Classification of Explainable Artificial Intelligence Methods through Their Output Formats

Vilone, G., & Longo, L. (2021) · Machine Learning and Knowledge Extraction · doi:10.3390/make3030032 · Cited by 157 · see citing works

Artificial cognition: How experimental psychology can help generate explainable artificial intelligence

Taylor, J. E. T., & Taylor, G. W. (2020) · Psychonomic Bulletin & Review · doi:10.3758/s13423-020-01825-5 · Cited by 135 · see citing works

A survey on XAI and natural language explanations

Cambria, E., Malandri, L., Mercorio, F., Mezzanzanica, M., & Nobani, N. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2022.103111 · Cited by 131 · see citing works

Explainable Object-Induced Action Decision for Autonomous Vehicles

Xu, Y., Yang, X., Gong, L., Lin, H.-C., Wu, T.-Y., Li, Y., & Vasconcelos, N. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00954 · Cited by 129 · see citing works

Progressive disclosure

Springer, A., & Whittaker, S. (2019) · Proceedings of the 24th International Conference on Intelligent User Interfaces · doi:10.1145/3301275.3302322 · Cited by 115 · see citing works

Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks

Räuker, T., Ho, A., Casper, S., & Hadfield-Menell, D. (2023) · 2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml54575.2023.00039 · Cited by 122 · see citing works

ExAID: A multimodal explanation framework for computer-aided diagnosis of skin lesions

Lucieri, A., Bajwa, M. N., Braun, S. A., Malik, M. I., Dengel, A. R., & Ahmed, S. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106620 · Cited by 107 · see citing works

SurvLIME: A method for explaining machine learning survival models

Kovalev, M. S., Utkin, L. V., & Kasimov, E. M. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106164 · Cited by 108 · see citing works

The Effects of Meaningful and Meaningless Explanations on Trust and Perceived System Accuracy in Intelligent Systems

Nourani, M., Kabir, S., Mohseni, S., & Ragan, E. D. (2019) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v7i1.5284 · Cited by 104 · see citing works

Adversarial Inference for Multi-Sentence Video Description

Park, J. S., Rohrbach, M., Darrell, T., & Rohrbach, A. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00676 · Cited by 94 · see citing works

Generative Counterfactual Introspection for Explainable Deep Learning

Liu, S., Kailkhura, B., Loveland, D., & Han, Y. (2019) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip45357.2019.8969491 · Cited by 106 · see citing works

Explainable Artificial Intelligence: a Systematic Review

Vilone, G., & Longo, L. (2020) · arXiv · doi:10.48550/arxiv.2006.00093 · Cited by 269 · see citing works

Adapting Grad-CAM for Embedding Networks

Chen, L., Chen, J., Hajimirsadeghi, H., & Mori, G. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093461 · Cited by 86 · see citing works

Explaining NLP Models via Minimal Contrastive Editing (MiCE)

Ross, A., Marasović, A., & Peters, M. E. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.336 · Cited by 93 · see citing works

SCOUT: Self-Aware Discriminant Counterfactual Explanations

Wang, P., & Vasconcelos, N. M. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00900 · Cited by 82 · see citing works

Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AIPreprint

Jacovi, A., Marasović, A., Miller, T., & Goldberg, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.07487 · Cited by 81 · see citing works

Multimodal Explainable Artificial Intelligence: A Comprehensive Review of Methodological Advances and Future Research Directions

Rodis, N., Sardianos, C., Grammatikis, P. I. R., Sarigiannidis, P. G., Varlamis, I., & Papadopoulos, G. T. (2024) · IEEE Access · doi:10.1109/access.2024.3467062 · Cited by 84 · see citing works

Multi-Level Multimodal Common Semantic Space for Image-Phrase Grounding

Akbari, H., Karaman, S., Bhargava, S., Chen, B., Vondrick, C., & Chang, S. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.01276 · Cited by 73 · see citing works

Faithful Multimodal Explanation for Visual Question Answering

Wu, J., & Mooney, R. (2019) · Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP · doi:10.18653/v1/w19-4812 · Cited by 73 · see citing works

Compressing Visual-linguistic Model via Knowledge Distillation

Fang, Z., Wang, J., Hu, X., Wang, L., Yang, Y., & Liu, Z. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00146 · Cited by 71 · see citing works

Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

(2019) · doi:10.18653/v1/w19-48 · Cited by 80 · see citing works

Revisiting The Evaluation of Class Activation Mapping for Explainability: A Novel Metric and Experimental Analysis

Poppi, S., Cornia, M., Baraldi, L., & Cucchiara, R. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00260 · Cited by 67 · see citing works

Probing Image-Language Transformers for Verb Understanding

Hendricks, L. A., & Nematzadeh, A. (2021) · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 · doi:10.18653/v1/2021.findings-acl.318 · Cited by 63 · see citing works

On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors

Lucieri, A., Bajwa, M. N., Braun, S. A., Malik, M. I., Dengel, A. R., & Ahmed, S. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9206946 · Cited by 67 · see citing works

Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language?

Hase, P., Zhang, S., Xie, H., & Bansal, M. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.390 · Cited by 62 · see citing works

A Multi-Component Framework for the Analysis and Design of Explainable Artificial Intelligence

Kim, M., Atakishiyev, S., Babiker, H. K. B., Farruque, N., Goebel, R. G., Zai͏̈ane, O. R., Motallebi, M.-H., Rabelo, J. C. B., Syed, T. I., Yao, H., et al. (2021) · Machine Learning and Knowledge Extraction · doi:10.3390/make3040045 · Cited by 57 · see citing works

Counterfactual Cycle-Consistent Learning for Instruction Following and Generation in Vision-Language Navigation

Wang, H., Liang, W. G., Shen, J., Gool, L. V., & Wang, W. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01503 · Cited by 57 · see citing works

Adversarial Robustness on In- and Out-Distribution Improves Explainability

Augustin, M., Meinke, A., & Hein, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58574-7_14 · Cited by 56 · see citing works

Cross Modality Bias in Visual Question Answering: A Causal View With Possible Worlds VQA

Vosoughi, A., Deng, S., Zhang, S., Tian, Y., Xu, C., & Luo, J. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3380259 · Cited by 57 · see citing works

Natural Language Rationales with Full-Stack Visual Reasoning: From Pixels to Semantic Frames to Commonsense Graphs

Marasović, A., Bhagavatula, C., Park, J. S., Bras, R. J. L., Smith, N. A., & Choi, Y. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.253 · Cited by 52 · see citing works

Advisable Learning for Self-Driving Vehicles by Internalizing Observation-to-Action Rules

Kim, J., Moon, S., Rohrbach, A., Darrell, T., & Canny, J. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00968 · Cited by 45 · see citing works

Not All Frames Are Equal: Weakly-Supervised Video Grounding With Contextual Similarity and Visual Clustering Losses

Shi, J., Xu, J., Gong, B., & Xu, C. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.01069 · Cited by 44 · see citing works

STEEX: Steering Counterfactual Explanations with Semantics

Jacob, P. M., Zablocki, É., Ben-younes, H., Chen, M., Pérez, P., & Cord, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19775-8_23 · Cited by 43 · see citing works

Untitled work

(2019) · doi:10.18653/v1/w19-4807 · Cited by 43 · see citing works

A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds

Kovalev, M., & Utkin, L. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.08.007 · Cited by 36 · see citing works

Learning to Explain With Complemental Examples

Kanehira, A., & Harada, T. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00880 · Cited by 39 · see citing works

The natural language explanation algorithms for the lung cancer computer-aided diagnosis system

Meldo, A., Utkin, L., Kovalev, M., & Kasimov, E. (2020) · Artif. Intell. Medicine · doi:10.1016/j.artmed.2020.101952 · Cited by 31 · see citing works

OCTET: Object-aware Counterfactual Explanations

Zemni, M., Chen, M., Zablocki, É., Ben-younes, H., Pérez, P., & Cord, M. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01446 · Cited by 38 · see citing works

Learning to Generate Grounded Visual Captions Without Localization Supervision

Ma, C., Kalantidis, Y., AlRegib, G., Vajda, P., Rohrbach, M., & Kira, Z. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58523-5_21 · Cited by 35 · see citing works

Multimodal Explanations by Predicting Counterfactuality in Videos

Kanehira, A., Takemoto, K., Inayoshi, S., & Harada, T. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00879 · Cited by 33 · see citing works

A Quantitative Evaluation of Global, Rule-Based Explanations of Post-Hoc, Model Agnostic Methods

Vilone, G., & Longo, L. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.717899 · Cited by 33 · see citing works

Explainability of vision-based autonomous driving systems: Review and challenges.Preprint

Zablocki, É., Ben-younes, H., Pérez, P., & Cord, M. (2021) · arXiv (Cornell University) · Cited by 32 · see citing works

A Deep Multi-Modal Explanation Model for Zero-Shot Learning

Liu, Y., & Tuytelaars, T. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2975980 · Cited by 31 · see citing works

Cross-modal text and visual generation: A systematic review. Part 1: Image to text

Żelaszczyk, M., & Mańdziuk, J. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.01.008 · Cited by 29 · see citing works

Semantically Interpretable Activation Maps: what-where-how explanations within CNNs

Marcos, D., Lobry, S., & Tuia, D. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00518 · Cited by 30 · see citing works

Shaping Visual Representations with Language for Few-Shot Classification

Mu, J., Liang, P., & Goodman, N. D. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.436 · Cited by 30 · see citing works

SurvNAM: The machine learning survival model explanation

Utkin, L. V., Satyukov, E. D., & Konstantinov, A. V. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.12.015 · Cited by 30 · see citing works

Explainable Image Classification: The Journey So Far and the Road Ahead

Kamakshi, V., & Krishnan, N. C. (2023) · AI · doi:10.3390/ai4030033 · Cited by 31 · see citing works

Prophet Attention: Predicting Attention with Future Attention

Liu, F., Ren, X., Wu, X., Ge, S., Fan, W., Zou, Y., & Sun, X. (2020) · neural information processing systems · Cited by 30 · see citing works

Sparse Visual Counterfactual Explanations in Image Space

Boreiko, V., Augustin, M., Croce, F., Berens, P., & Hein, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16788-1_9 · Cited by 27 · see citing works

Deep Learning on MRI Affirms the Prominence of the Hippocampal Formation in Alzheimer's Disease ClassificationPreprint

Feng, X., Yang, J., Lipton, Z. C., Small, S. A., & Provenzano, F. A. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/456277 · Cited by 27 · see citing works

Intelligible Artificial Intelligence

Weld, D. S., & Bansal, G. (2018) · Cited by 26 · see citing works

Generative Counterfactual Introspection for Explainable Deep LearningPreprint

Liu, S., Kailkhura, B., Loveland, D., & Han, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.03077 · Cited by 26 · see citing works

XEdgeAI: A human-centered industrial inspection framework with data-centric Explainable Edge AI approach

Nguyen, T. T. H., Nguyen, L. P. T., & Cao, H. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102782 · Cited by 25 · see citing works

Comprehensible Convolutional Neural Networks via Guided Concept Learning

Wickramanayake, S., Hsu, W., & Lee, M. L. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9534269 · Cited by 25 · see citing works

Counterfactual Explanation of Machine Learning Survival Models

Kovalev, M., Utkin, L., Coolen, F. P. A., & Konstantinov, A. (2020) · Informatica · doi:10.15388/21-infor468 · Cited by 27 · see citing works

Incremental Residual Concept Bottleneck Models

Shang, C., Zhou, S., Zhang, H., Ni, X., Yang, Y., & Wang, Y. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01049 · Cited by 22 · see citing works

Grounding Visual Representations with Texts for Domain Generalization

Min, S., Park, N., Kim, S., Park, S., & Kim, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19836-6_3 · Cited by 22 · see citing works

A Generalized Explanation Framework for Visualization of Deep Learning Model Predictions

Wang, P., & Vasconcelos, N. M. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3241106 · Cited by 21 · see citing works

Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and Datasets

Choe, J., Oh, S. J., Chun, S., Lee, S., Akata, Z., & Shim, H. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3169881 · Cited by 22 · see citing works

CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations

Salewski, L., Koepke, A. S., Lensch, H. P. A., & Akata, Z. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-04083-2_5 · Cited by 20 · see citing works

Learning Decision Trees Recurrently Through Communication

Alaniz, S., Marcos, D., Schiele, B., & Akata, Z. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01331 · Cited by 22 · see citing works

Hierarchical Explanations for Video Action Recognition

Gulshad, S., Long, T., & van Noord, N. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00379 · Cited by 19 · see citing works

Where is the Model Looking At? – Concentrate and Explain the Network Attention

Xu, W., Wang, J., Wang, Y., Xu, G., Lin, D., Dai, W., & Wu, Y. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.2987729 · Cited by 19 · see citing works

Explanation as a Process: User-Centric Construction of Multi-level and Multi-modal Explanations

Finzel, B., Tafler, D. E., Scheele, S., & Schmid, U. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87626-5_7 · Cited by 20 · see citing works

Class Feature Pyramids for Video Explanation

Stergiou, A. G., Kapidis, G., Kalliatakis, G., Chrysoulas, C., Poppe, R. W., Veltkamp, R. C., Multimedia, S., & Multimedia (2019) · Utrecht University Repository (Utrecht University) · Cited by 18 · see citing works

Explainable AI: A Neurally-Inspired Decision Stack Framework

Khan, M. S., Nayebpour, M., Li, M., El‐Amine, H., Koizumi, N., & Olds, J. L. (2022) · Biomimetics · doi:10.3390/biomimetics7030127 · Cited by 16 · see citing works

A First Look: Towards Explainable TextVQA Models via Visual and Textual Explanations

Rao, V. N., Zhen, X., Hovsepian, K., & Shen, M. (2021) · doi:10.18653/v1/2021.maiworkshop-1.4 · Cited by 17 · see citing works

Learning Interpretable, High-Performing Policies for Autonomous Driving

Paleja, R., Niu, Y., Silva, A., Ritchie, C., Choi, S., & Gombolay, M. (2022) · Robotics: Science and Systems · doi:10.15607/rss.2022.xviii.068 · Cited by 15 · see citing works

Towards counterfactual and contrastive explainability and transparency of DCNN image classifiers

Tariq, S. A., Zia, T., & Ghafoor, M. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109901 · Cited by 15 · see citing works

Why Do These Match? Explaining the Behavior of Image Similarity Models

Plummer, B. A., Vasileva, M. I., Petsiuk, V., Saenko, K., & Forsyth, D. A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58621-8_38 · Cited by 15 · see citing works

Grounding Open-Domain Instructions to Automate Web Support Tasks

Xu, N., Masling, S., Du, M., Campagna, G., Heck, L. P., Landay, J. A., & Lam, M. S. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.80 · Cited by 23 · see citing works

Winsor-CAM: Human-Tunable Visual Explanations From Deep Networks via Layer-Wise Winsorization

Wall, C., Wang, L., Rizk, R., & Santosh, K. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3668075 · Cited by 15 · see citing works

Counterfactual-based Saliency Map: Towards Visual Contrastive Explanations for Neural Networks

Wang, X., Wang, Z., Weng, H., Guo, H., Zhang, Z., Jin, L., Wei, T., & Ren, K. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00195 · Cited by 14 · see citing works

Review of Research in the Field of Developing Methods to Extract Rules From Artificial Neural Networks

Averkin, A. N., & Yarushev, S. (2021) · Journal of Computer and Systems Sciences International · doi:10.1134/s1064230721060046 · Cited by 13 · see citing works

Why Does My Model Fail? Contrastive Local Explanations for Retail Forecasting

Lučić, A., Haned, H., & de Rijke, M. (2019) · UvA-DARE (University of Amsterdam) · doi:10.48550/arxiv.1908.00085 · Cited by 13 · see citing works

A Study on Multimodal and Interactive Explanations for Visual Question AnsweringPreprint

Alipour, K., Schulze, J. P., Yao, Y., Ziskind, A. J., & Burachas, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.00431 · Cited by 13 · see citing works

Generating visual explanations with natural language

Hendricks, L. A., Rohrbach, A., Schiele, B., Darrell, T., & Akata, Z. (2021) · Applied AI Letters · doi:10.1002/ail2.55 · Cited by 12 · see citing works

Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User DemonstrationsPreprint

Paleja, R., Silva, A., Chen, L., & Gombolay, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.06397 · Cited by 12 · see citing works

Driving through the Concept Gridlock: Unraveling Explainability Bottlenecks in Automated Driving

Echterhoff, J. M., Yan, A., Han, K., Abdelraouf, A., Gupta, R., & McAuley, J. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00718 · Cited by 11 · see citing works

Object Grounding via Iterative Context Reasoning

Chen, L., Zhai, M., He, J., & Mori, G. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00177 · Cited by 11 · see citing works

The Impact of Explanations on AI Competency Prediction in VQA

Alipour, K., Ray, A., Lin, X., Schulze, J. P., Yao, Y., & Burachas, G. (2020) · doi:10.1109/hccai49649.2020.00010 · Cited by 10 · see citing works

Explaining with Counter Visual Attributes and Examples

Gulshad, S., & Smeulders, A. W. M. (2020) · International Conference on Multimedia Retrieval · doi:10.1145/3372278.3390672 · Cited by 11 · see citing works

Proceedings of the Third Workshop on Multimodal Artificial Intelligence

(2021) · doi:10.18653/v1/2021.maiworkshop-1 · Cited by 11 · see citing works

Object Recognition as Next Token Prediction

Yue, K., Chen, B.-C., Geiping, J., Li, H., Goldstein, T., & Lim, S.-N. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01575 · Cited by 10 · see citing works

Testing methods of neural systems understanding

Lindsay, G. W., & Bau, D. (2023) · Cognitive Systems Research · doi:10.1016/j.cogsys.2023.101156 · Cited by 10 · see citing works

Grounding Visual Explanations (Extended Abstract)Preprint

Hendricks, L. A., Hu, R., Darrell, T., & Akata, Z. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.06465 · Cited by 3 · see citing works

Generating visual explanations with natural language

Hendricks, L. A., Rohrbach, A., Schiele, B., Darrell, T., & Akata, Z. (2021) · Applied AI Letters · doi:10.1002/ail2.55 · Cited by 12 · see citing works

Generating Visual Explanations

Hendricks, L. A., Akata, Z., Rohrbach, M., Donahue, J., Schiele, B., & Darrell, T. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_1 · Cited by 534 · see citing works

Generating Counterfactual Explanations with Natural LanguagePreprint

Hendricks, L. A., Hu, R., Darrell, T., & Akata, Z. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.09809 · Cited by 81 · see citing works

Generating Natural Counterfactual Visual Explanations

Zhao, W., Oyama, S., & Kurihara, M. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/742 · Cited by 10 · see citing works