Generating Visual Explanations

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · 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

BLEU: a method for automatic evaluation of machine translation

Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344 · see citing works

Caffe: Convolutional Architecture for Fast Feature Embedding

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077 · 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

Show and tell: A neural image caption generator

Vinyals, O., Toshev, A., Bengio, S., & Erhan, D. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298935 · Cited by 6,318 · see citing works

Long-term recurrent convolutional networks for visual recognition and description

Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Darrell, T., & Saenko, K. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298878 · Cited by 5,228 · see citing works

Deep visual-semantic alignments for generating image descriptions

Karpathy, A., & Fei-Fei, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298932 · Cited by 4,902 · see citing works

CIDEr: Consensus-based image description evaluation

Vedantam, R., Zitnick, C. L., & Parikh, D. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299087 · Cited by 4,845 · see citing works

Introduction to WordNet: An On-line Lexical Database *

Miller, G. A., Beckwith, R., Fellbaum, C., Gross, D., & Miller, K. J. (1990) · International Journal of Lexicography · doi:10.1093/ijl/3.4.235 · Cited by 4,197 · see citing works

Untitled work

(2005) · Cited by 4,152 · see citing works

Caltech-UCSD Birds-200-2011 Dataset

Wah, C. (2024) · CaltechAUTHORS (California Institute of Technology) · doi:10.57702/i9oeewf6 · Cited by 3,875 · see citing works

Generative Adversarial Text to Image Synthesis

Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., & Lee, H. (2016) · arXiv · doi:10.48550/arxiv.1605.05396 · Cited by 2,494 · see citing works

Attribute-Based Classification for Zero-Shot Visual Object Categorization

Lampert, C. H., Nickisch, H., & Stefan Harmeling, B. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.140 · Cited by 1,639 · 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

From captions to visual concepts and back

Fang, H., Gupta, S., Iandola, F., Srivastava, R. K., Deng, L., Dollár, P., Gao, J., He, X., Mitchell, M., Platt, J. C., et al. (2015) · 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.2015.7298754 · Cited by 1,305 · 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

Long-term Recurrent Convolutional Networks for Visual Recognition and Description

Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., & Darrell, T. (2014) · doi:10.21236/ada623249 · Cited by 1,081 · see citing works

Compact Bilinear Pooling

Gao, Y., Beijbom, O., Zhang, N., & Darrell, T. (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.2016.41 · Cited by 912 · 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

What makes Paris look like Paris?

Doersch, C., Singh, S., Gupta, A., Šivic, J., & Efros, A. A. (2012) · ACM Transactions on Graphics · doi:10.1145/2185520.2185597 · Cited by 693 · see citing works

Multimodal Neural Language Models

Kiros, R., Salakhutdinov, R., & Zemel, R. (2014) · Cited by 593 · see citing works

Baby talk: Understanding and generating simple image descriptions

Kulkarni, G. A., Premraj, V., Dhar, S., Li, S., Choi, Y., Berg, A. C., & Berg, T. L. (2011) · doi:10.1109/cvpr.2011.5995466 · Cited by 521 · see citing works

YouTube2Text: Recognizing and Describing Arbitrary Activities Using Semantic Hierarchies and Zero-Shot Recognition

Guadarrama, S., Krishnamoorthy, N., Malkarnenkar, G., Venugopalan, S., Mooney, R. J., Darrell, T., & Saenko, K. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.337 · Cited by 500 · see citing works

Guiding the Long-Short Term Memory Model for Image Caption Generation

Jia, X., Gavves, E., Fernando, B., & Tuytelaars, T. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.277 · Cited by 469 · see citing works

Learning to Compose Neural Networks for Question Answering

Andreas, J., Rohrbach, M., Darrell, T., & Klein, D. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1181 · Cited by 450 · see citing works

Explain Images with Multimodal Recurrent Neural NetworksPreprint

Mao, J., Xu, W., Yang, Y., Wang, J., & Yuille, A. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1410.1090 · Cited by 423 · see citing works

Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos

Yeung, S. Y., Russakovsky, O., Jin, N., Andriluka, M., Mori, G., & Fei-Fei, L. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-017-1013-y · Cited by 403 · 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

Deep Compositional Captioning: Describing Novel Object Categories without Paired Training Data

Hendricks, L. A., Venugopalan, S., Rohrbach, M., Mooney, R. J., Saenko, K., & Darrell, T. (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.2016.8 · Cited by 280 · see citing works

A review of explanation methods for Bayesian networks

Lacave, C., & Díez, F. J. (2002) · The Knowledge Engineering Review · doi:10.1017/s026988890200019x · Cited by 240 · 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

Learning Like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images

Mao, J., Xu, W., Yang, Y., Wang, J., Huang, Z., & Yuille, A. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.291 · Cited by 147 · 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

Agents that Learn to Explain Themselves

Johnson, W. L. (1994) · Cited by 93 · see citing works

Explaining robot actions

Lomas, M., Chevalier, R., Cross, E., Garrett, R. C., Hoare, J., & Kopack, M. (2012) · IEEE/ACM International Conference on Human-Robot Interaction · doi:10.1145/2157689.2157748 · Cited by 88 · see citing works

Use and Impact of Computers in Clinical Medicine

Anderson, J. G., & Jay, S. J. (1987) · Computers and medicine · doi:10.1007/978-1-4613-8674-2 · Cited by 80 · see citing works

Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of ImagesPreprint

Mao, J., Xu, W., Yang, Y. P., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.06692 · Cited by 63 · 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

How Do You Tell a Blackbird from a Crow?

Berg, T., & Belhumeur, P. N. (2013) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.9 · Cited by 35 · see citing works

Use and Impact of Computers in Clinical Medicine

Carpenter, P. C. (1987) · Mayo Clinic Proceedings · doi:10.1016/s0025-6196(12)65235-5 · Cited by 5 · see citing works

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423 · see citing works

Explaining Explanations: An Overview of Interpretability of Machine Learning

Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., & Kagal, L. (2018) · 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2018.00018 · Cited by 2,507 · see citing works

Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence

Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., Guidotti, R., Del Ser, J., Díaz-Rodríguez, N., & Herrera, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101805 · Cited by 1,762 · see citing works

Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications

Samek, W., Montavon, G., Lapuschkin, S., Anders, C. J., & Muller, K.-R. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3060483 · Cited by 1,477 · see citing works

Explainable artificial intelligence: A survey

Dosilovic, F. K., Brcic, M., & Hlupic, N. (2018) · 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) · doi:10.23919/mipro.2018.8400040 · Cited by 1,147 · see citing works

Drug discovery with explainable artificial intelligence

Jiménez-Luna, J., Grisoni, F., & Schneider, G. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-00236-4 · Cited by 1,063 · see citing works

A Survey on the Explainability of Supervised Machine Learning

Burkart, N., & Huber, M. F. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12228 · Cited by 1,008 · see citing works

Explainable artificial intelligence: a comprehensive review

Minh, D., Wang, H. X., Li, Y. F., & Nguyen, T. N. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10088-y · Cited by 983 · see citing works

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

Gatt, A., & Krahmer, E. (2018) · Journal of Artificial Intelligence Research · doi:10.1613/jair.5477 · Cited by 776 · see citing works

Explainable AI: A Brief Survey on History, Research Areas, Approaches and Challenges

Xu, F., Uszkoreit, H., Du, Y., Fan, W., Zhao, D., & Zhu, J. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-32236-6_51 · Cited by 758 · see citing works

Evaluating the quality of machine learning explanations: a survey on methods and metrics

Zhou, J., Gandomi, A. H., Chen, F., & Holzinger, A. (2021) · Electronics · doi:10.3390/electronics10050593 · Cited by 606 · see citing works

Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering

Goyal, Y., Khot, T., Agrawal, A., Summers-Stay, D., Batra, D., & Parikh, D. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-018-1116-0 · Cited by 487 · see citing works

Improved Image Captioning via Policy Gradient optimization of SPIDEr

Liu, S., Zhu, Z., Ye, N., Guadarrama, S., & Murphy, K. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.100 · Cited by 430 · see citing works

A Review of Trustworthy and Explainable Artificial Intelligence (XAI)

Chamola, V., Hassija, V., Sulthana, A. R., Ghosh, D., Dhingra, D., & Sikdar, B. (2023) · IEEE Access · doi:10.1109/access.2023.3294569 · Cited by 352 · see citing works

Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems

Ling, W., Yogatama, D., Dyer, C., & Blunsom, P. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1015 · Cited by 325 · see citing works

One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques

Arya, V., Bellamy, R. K. E., Chen, P.-Y., Dhurandhar, A., Hind, M., Hoffman, S. C., Houde, S., Liao, Q. V., Luss, R., Mojsilović, A., et al. (2019) · arXiv · doi:10.48550/arxiv.1909.03012 · Cited by 268 · see citing works

Explainable AI: The New 42?

Goebel, R., Chander, A., Holzinger, K., Lecue, F., Akata, Z., Stumpf, S., Kieseberg, P., & Holzinger, A. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-99740-7_21 · Cited by 259 · see citing works

The neuroconnectionist research programme

Doerig, A., Sommers, R. P., Seeliger, K., Richards, B., Ismael, J., Lindsay, G. W., Kording, K. P., Konkle, T., van Gerven, M. A. J., Kriegeskorte, N., et al. (2023) · Nature Reviews Neuroscience · doi:10.1038/s41583-023-00705-w · Cited by 233 · see citing works

The challenge of crafting intelligible intelligence

Weld, D. S., & Bansal, G. (2019) · Communications of the ACM · doi:10.1145/3282486 · Cited by 253 · 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

Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differences

Wang, R., Harper, F. M., & Zhu, H. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376813 · Cited by 211 · see citing works

A Review on Explainability in Multimodal Deep Neural Nets

Joshi, G., Walambe, R., & Kotecha, K. (2021) · IEEE Access · doi:10.1109/access.2021.3070212 · Cited by 204 · see citing works

Grounding visual explanations

Hendricks, L. A., Hu, R., Darrell, T., & Akata, Z. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01216-8_17 · Cited by 171 · see citing works

Using Machine Learning to Support Qualitative Coding in Social Science: Shifting the Focus to Ambiguity

Chen, N.-C., Drouhard, M., Kocielnik, R., Suh, J., & Aragon, C. R. (2018) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3185515 · Cited by 152 · see citing works

Can language models learn from explanations in context?

Lampinen, A., Dasgupta, I., Chan, S., Mathewson, K., Tessler, M., Creswell, A., McClelland, J., Wang, J., & Hill, F. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022 · doi:10.18653/v1/2022.findings-emnlp.38 · Cited by 142 · see citing works

CheXplain: Enabling Physicians to Explore and Understand Data-Driven, AI-Enabled Medical Imaging Analysis

Xie, Y., Chen, M., Kao, D., Gao, G., & Chen, X. '. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376807 · Cited by 141 · 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

Explainable Artificial Intelligence: a Systematic Review

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

Explaining Machine Learning Models for Clinical Gait Analysis

Slijepcevic, D., Horst, F., Lapuschkin, S., Horsak, B., Raberger, A.-M., Kranzl, A., Samek, W., Breiteneder, C., Schöllhorn, W. I., & Zeppelzauer, M. (2022) · ACM Transactions on Computing for Healthcare · doi:10.1145/3474121 · Cited by 82 · see citing works

Keeping Community in the Loop: Understanding Wikipedia Stakeholder Values for Machine Learning-Based Systems

Smith, C. E., Yu, B., Srivastava, A., Halfaker, A., Terveen, L., & Zhu, H. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376783 · Cited by 77 · 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

e-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Tasks

Kayser, M., Camburu, O.-M., Salewski, L., Emde, C., Do, V., Akata, Z., & Lukasiewicz, T. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00128 · Cited by 68 · see citing works

Explainable Sentiment Analysis with Applications in Medicine

Zucco, C., Liang, H., Fatta, G. D., & Cannataro, M. (2018) · 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2018.8621359 · Cited by 59 · 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

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

Kandinsky Patterns

Müller, H., & Holzinger, A. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103546 · Cited by 30 · see citing works

Robustness Analysis of Deep Learning-Based Lung Cancer Classification Using Explainable Methods

Malafaia, M., Silva, F., Neves, I., Pereira, T., & Oliveira, H. P. (2022) · IEEE Access · doi:10.1109/access.2022.3214824 · Cited by 25 · see citing works

An overview of key trustworthiness attributes and KPIs for trusted ML-based systems engineering

Mattioli, J., Sohier, H., Delaborde, A., Amokrane-Ferka, K., Awadid, A., Chihani, Z., Khalfaoui, S., & Pedroza, G. (2024) · AI and Ethics · doi:10.1007/s43681-023-00394-2 · Cited by 22 · see citing works

Group Cognition and Collaborative AI

Koch, J., & Oulasvirta, A. (2018) · Human–Computer Interaction Series · doi:10.1007/978-3-319-90403-0_15 · Cited by 15 · see citing works

Rigorously assessing natural language explanations of neurons

Huang, J., Geiger, A., D’Oosterlinck, K., Wu, Z., & Potts, C. (2023) · Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP · doi:10.18653/v1/2023.blackboxnlp-1.24 · Cited by 3 · see citing works

Nomological Deductive Reasoning for Trustworthy, Human-Readable, and Actionable AI Outputs

Hakizimana, G., & Ledezma Espino, A. (2025) · Algorithms · doi:10.3390/a18060306 · Cited by 2 · see citing works

OSCAR and ActivityNet: an Image Captioning model can effectively learn a Video Captioning dataset

Byrd, E., & González-Mendoza, M. (2021) · LatinX in AI at Computer Vision and Pattern Recognition Conference (LXAI) · doi:10.52591/lxai202106257 · Cited by 1 · see citing works

Image Captioning Methodologies Using Deep Learning: A Review

Meghwal, V. K., Mittal, N., & Singh, G. (2020) · Lecture notes in electrical engineering · doi:10.1007/978-981-15-7031-5_48

Boosted Attention: Leveraging Human Attention for Image CaptioningPreprint

Chen, S., & Zhao, Q. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.00767 · Cited by 1 · see citing works

Video Captioning via Hierarchical Reinforcement LearningPreprint

Wang, X., Chen, W., Wu, J., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.11135 · Cited by 22 · see citing works

Image Captioning using Neural Networks

Shah, D., Shaikh, V., Verma, G., et al. (2022) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-5935