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Grounding Visual Explanations
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“In addition, Gatt and Krahmer make a distinction between ''objective'' (automatic, corpus-based) and ''subjective'' (human judgements) metrics [164].”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [164]
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Citations
Cites
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
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
"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
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
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
Williams, R. J. (1992) · Machine Learning · doi:10.1007/bf00992696 · Cited by 8,608
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
“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
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
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
Self-citation
Research works printed but not linked: 2
- Biran, O., McKeown, K.: Justification narratives for individual classifications. In: Proceedings of the AutoML Workshop at ICML, vol. 2014 (2014)
- Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. https://arxiv.org/abs/1610.02391 v3 7(8) (2016) · arXiv
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Stergiou, A., Kapidis, G., Kalliatakis, G., Chrysoulas, C., Poppe, R. W., & Veltkamp, R. C. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00524 · Cited by 19
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Lin, C.-S., & Wang, Y.-C. F. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3268001 · Cited by 9
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Nishida, K., Nishida, K., & Nishioka, S. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.106 · Cited by 10
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Contextual Prediction Difference Analysis.Preprint
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Utkin, L. V., DROBINTSEV, P. D., Kovalev, M. S., & Konstantinov, A. V. (2021) · Conference of Open Innovations Association (FRUCT) · doi:10.23919/fruct50888.2021.9347612 · Cited by 5
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Wang, P., & Vasconcelos, N. M. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00490 · Cited by 5
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CAPTION: Caption Analysis with Proposed Terms, Image of Objects, and Natural Language Processing
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Interpreting Adversarial Examples with AttributesPreprint
Gulshad, S., Metzen, J. H., Smeulders, A. W. M., & Akata, Z. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.08279 · Cited by 5
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Vosoughi, A., Bondi, L., Wu, H.-H., & Xu, C. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446736 · Cited by 5
Navigating the Structured What-If Spaces: Counterfactual Generation via Structured Diffusion
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When neuro-robots go wrong: A review
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Utkin, L. V., Мелдо, А. А., Kovalev, M. S., & Kasimov, E. M. (2020) · IEEE International Conference on Soft Computing and Measurements (SCM) · doi:10.1109/scm50615.2020.9198764 · Cited by 4
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Jang, M., Majumder, B. P., McAuley, J., Lukasiewicz, T., & Camburu, O.-M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.47 · Cited by 4
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Lin, J., Jain, U., & Schwing, A. G. (2019)
Utkin, L. V., Petrov, A., & Konstantinov, A. V. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-20875-1_6
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Utkin, L. V., Kirpichenko, S. R., Konstantinov, A. V., Zaborovsky, V. S., Orlov, I. N., & Verbova, N. M. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-13612-1_10
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Pierrard, R. (2020) · theses.fr (ABES) · doi:10.70675/999aa361z016fz4e8ez90efz1c93ba7690a8
Multimodal and interactive models for visually grounded language learning
Strub, F. (2020) · theses.fr (ABES) · doi:10.70675/565f6b44z5446z433azb85dz57d7ca36bccf
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Catanpatan, W. Y., Aballe, Z., & Maderazo, C. V. (2026) · International Conference on Multi-scale Artificial Intelligence (MAI) · doi:10.1109/mai69289.2026.11543767
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Liu, J., Shen, Z., He, Y., Zhang, X., Xu, R., Yu, H., & Cui, P. (2021) · arXiv · doi:10.48550/arxiv.2108.13624 · Cited by 253
Metadata
- Authors
- Hendricks, L. A., Hu, R., Darrell, T., & Akata, Z.
- Affiliations
- University of California, Berkeley (US)
- University of Amsterdam (NL)
- Corresponding author
- Hendricks, Lisa Anne
- Venue
- Lecture notes in computer science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 269-286
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2883512601
- Cited by
- 171
- References
- 36
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 19 · 2023: 19 · 2022: 20 · 2021: 38 · 2020: 34 · 2019: 30 · 2018: 3
- FWCI
- 10.72
- Type
- conference paper
- Language
- en
- Open access
- green
- handle.uba.uva.nl
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- UvA-DARE (University of Amsterdam) (publishedVersion, other-oa)
- arXiv (Cornell University) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- Defense Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- counterfactual explanations, fine-grained classification, foil, sentence correction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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