The DOI matches its Crossref record; 63 of the 65 references it cites were found in a registry or an index.
VQA: Visual Question Answering
Abstract
We propose the task of free-form and open-ended Visual Question Answering (VQA). Given an image and a natural language question about the image, the task is to provide an accurate natural language answer. Mirroring real-world scenarios, such as helping the visually impaired, both the questions and answers are open-ended. Visual questions selectively target different areas of an image, including background details and underlying context. As a result, a system that succeeds at VQA typically needs a more detailed understanding of the image and complex reasoning than a system producing generic image captions. Moreover, VQA is amenable to automatic evaluation, since many open-ended answers contain only a few words or a closed set of answers that can be provided in a multiple-choice format. We provide a dataset containing ~0.25M images, ~0.76M questions, and ~10M answers (www.visualqa.org), and discuss the information it provides. Numerous baselines for VQA are provided and compared with human performance.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“This level of expert disagreement is in line with what is observed in other well-known machine learning benchmarks59–62.”
From Humanity's Last Exam (2025) · cites this paper as [60]
In the citing paper
“VQA models (e.g., Antol et al., 2015) are able to give a visual answer to a verbal question; for example, in response to the question “How many giraffes are drinking water?” and a given image, the network would respond by drawing bounding boxes around all the giraffes drinking from the water hole (but not the other ones, even if they are standing by the water hole).”
From Towards a Smart Bionic Eye : AI-powered artificial vision for the treatment of incurable blindness (2022) · cites this paper as Antol et al., 2015
In the citing paper
“Although VQA has traditionally been treated as a task with a single ground-truth label [4], recent work highlights the inherent subjectivity in visual information seeking, showing that blind users integrate multiple perspectives to shape their visual understanding, whether with AI tools or humans [94].”
From Disability-First AI Dataset Annotation: Co-designing Stuttered Speech Annotation Guidelines with People Who Stutter (2026) · cites this paper as [4]
In the citing paper
“Most datasets that are currently available for training question generation systems are crowd-sourced [42, 47, 84, 92, 114, 115, 119, 120, 122, 123], which largely impacts the quality of generated questions.”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [84]
Citations
Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
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
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
Self-citationDistributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
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
Caffe: Convolutional Architecture for Fast Feature EmbeddingPreprint
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.5093 · Cited by 5,299
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
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
Freebase: a collaboratively created graph database for structuring human knowledge
Bollacker, K., Evans, C., Paritosh, P., Sturge, T., & Taylor, J. (2008) · Proceedings of the 2008 ACM SIGMOD international conference on Management of data · doi:10.1145/1376616.1376746 · Cited by 4,852
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
Self-citation
Research works printed but not linked: 2
- lin, 2015, Don't just listen, use your imagination: Leveraging visual common sense for non-visual tasks, CVPR
- lenat, 1989, Building Large Knowledge-Based Systems Representation and Inference in the CYC Project
Cited by
Sorted by citations
Items 601–700 of 1,027
Split, Embed and Merge: An accurate table structure recognizer
Zhang, Z., Zhang, J., Du, J., & Wang, F. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108565 · Cited by 84
UATVR: Uncertainty-Adaptive Text-Video Retrieval
Fang, B., Wu, W., Liu, C., Zhou, Y., Song, Y., Wang, W., Shu, X., Ji, X., & Wang, J. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01262 · Cited by 76
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 105
Qi, Y., Pan, Z., Hong, Y., Yang, M.-H., van den Hengel, A., & Wu, Q. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00168 · Cited by 80
Structured Two-Stream Attention Network for Video Question Answering
Gao, L., Zeng, P., Song, J., Li, Y.-F., Liu, W., Mei, T., & Shen, H. T. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33016391 · Cited by 73
WebQA: Multihop and Multimodal QA
Chang, Y., Cao, G., Narang, M., Gao, J., Suzuki, H., & Bisk, Y. (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/cvpr52688.2022.01600 · Cited by 110
Revealing Single Frame Bias for Video-and-Language Learning
Lei, J., Berg, T. L., & Bansal, M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.29 · Cited by 146
Open Vocabulary Semantic Segmentation with Patch Aligned Contrastive Learning
Mukhoti, J., Lin, T., Poursaeed, O., Wang, R., Shah, A., Torr, P. H. S., & Lim, S.-N. (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/cvpr52729.2023.01860 · Cited by 90
Vman: visual-modified attention network for multimodal paradigms
Song, X., Han, D., Chen, C., Shen, X., & Wu, H. (2024) · The Visual Computer · doi:10.1007/s00371-024-03563-4 · Cited by 75
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
An Improved Attention for Visual Question Answering
Rahman, T., Chou, S.-H., Sigal, L., & Carenini, G. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00181 · Cited by 74
Active visual information gathering for vision-language navigation
Wang, H., Wang, W., Shu, T., Liang, W., & Shen, J. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58542-6_19 · Cited by 74
Bilinear Graph Networks for Visual Question Answering
Guo, D., Xu, C., & Tao, D. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3104937 · Cited by 81
PromptCap: Prompt-Guided Image Captioning for VQA with GPT-3
Hu, Y., Hua, H., Yang, Z., Shi, W., Smith, N. A., & Luo, J. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00277 · Cited by 194
BLINK: Multimodal Large Language Models Can See but Not Perceive
Fu, X., Hu, Y., Li, B., Feng, Y., Wang, H., Lin, X., Roth, D., Smith, N. A., Ma, W.-C., & Krishna, R. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73337-6_9 · Cited by 345
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
Learnable Aggregating Net with Diversity Learning for Video Question Answering
Li, X., Gao, L., Wang, X., Liu, W., Xu, X., Shen, H. T., & Song, J. (2019) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3343031.3350971 · Cited by 73
Beyond Short-Term Snippet: Video Relation Detection With Spatio-Temporal Global Context
Liu, C., Yang, J., Xu, K., Gong, G., & Mu, Y. (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/cvpr42600.2020.01085 · Cited by 75
Wang, T., Li, J., Kong, Z., Liu, X., Snoussi, H., & Lv, H. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.07.011 · Cited by 73
Beyond Question-Based Biases: Assessing Multimodal Shortcut Learning in Visual Question Answering
Dancette, C., Cadène, R., Teney, D., & Cord, M. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00160 · Cited by 89
An End-to-End Multimodal Voice Activity Detection Using WaveNet Encoder and Residual Networks
Ariav, I., & Cohen, I. (2019) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2019.2901195 · Cited by 74
Coreset-Based Neural Network Compression
Dubey, A., Chatterjee, M., & Ahuja, N. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01234-2_28 · Cited by 72
Self-Adaptive Neural Module Transformer for Visual Question Answering
Zhong, H., Chen, J., Shen, C., Zhang, H., Huang, J., & Hua, X. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.2995278 · Cited by 73
Zhang, S., Jiang, T., Wang, T., Kuang, K., Zhao, Z., Zhu, J., Yu, J., Yang, H., & Wu, F. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3413518 · Cited by 77
Show, Deconfound and Tell: Image Captioning with Causal Inference
Liu, B., Wang, D., Yang, X., Zhou, Y. Z., Yao, R., Shao, Z., & Zhao, J. (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/cvpr52688.2022.01751 · Cited by 75
Context Understanding in Computer Vision: A Survey
Wang, X., & Zhu, Z. (2022) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2023.103646 · Cited by 85
From Pixels to Objects: Cubic Visual Attention for Visual Question Answering
Song, J., Zeng, P., Gao, L., & Shen, H. T. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/126 · Cited by 77
Deep Attention Neural Tensor Network for Visual Question Answering
Bai, Y., Fu, J., Zhao, T., & Mei, T. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01258-8_2 · Cited by 68
Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directions
Mai, G., Janowicz, K., Zhu, R., Cai, L., & Lao, N. (2021) · AGILE GIScience Series · doi:10.5194/agile-giss-2-8-2021 · Cited by 86
Structured Multimodal Attentions for TextVQA
Gao, C., Zhu, Q., Wang, P., Li, H., Liu, Y., van den Hengel, A., & Wu, Q. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3132034 · Cited by 76
Pathological Visual Question AnsweringPreprint
He, X., Cai, Z., Wei, W., Zhang, Y., Mou, L., Xing, E. P., & Xie, P. (2020) · doi:10.36227/techrxiv.13127537 · Cited by 90
XGPT: Cross-modal Generative Pre-Training for Image Captioning
Xia, Q., Huang, H., Duan, N., Zhang, D., Ji, L., Sui, Z., Cui, E., Bharti, T., & Zhou, M. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-88480-2_63 · Cited by 70
Learning Common Sense through Visual Abstraction
Vedantam, R., Lin, X., Batra, T., Zitnick, C. L., & Parikh, D. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.292 · Cited by 72
Self-citationCompressing 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 81
FMFN: Fine-Grained Multimodal Fusion Networks for Fake News Detection
Wang, J., Mao, H., & Li, H. (2022) · Applied Sciences · doi:10.3390/app12031093 · Cited by 76
Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning
Gao, Y., Gu, S., Jiang, J., Hong, S.-S., Yu, D., & Zhao, L. (2024) · ACM Computing Surveys · doi:10.1145/3644073 · Cited by 85
Attentive and Adversarial Learning for Video Summarization
Fu, T.-J., Tai, S.-H., & Chen, H.-T. (2019) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2019.00173 · Cited by 71
A Simple Baseline for Audio-Visual Scene-Aware Dialog
Schwartz, I., Schwing, A. G., & Hazan, 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.2019.01283 · Cited by 71
Focal Visual-Text Attention for Memex Question Answering
Liang, J., Jiang, L., Cao, L., Kalantidis, Y., Li, L.-J., & Hauptmann, A. G. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2890628 · Cited by 70
Learning Visual Knowledge Memory Networks for Visual Question Answering
Zhou, S., Zhu, C., Dong, Y., Cai, D., Chen, Y., & Li, J. (2018) · 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.2018.00807 · Cited by 70
Zhang, W., Yu, J., Zhao, W., & Ran, C. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.02.006 · Cited by 70
Image captioning for effective use of language models in knowledge-based visual question answering
Salaberria, A., Azkune, G., de Lacalle, O. L., Soroa, A., & Agirre, E. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118669 · Cited by 78
Vision Guided Generative Pre-trained Language Models for Multimodal Abstractive Summarization
Yu, T., Dai, W., Liu, Z., & Fung, P. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.326 · Cited by 78
A Survey on Explainable Artificial Intelligence Techniques and Challenges
Hanif, A., Zhang, X., & Wood, S. (2021) · IEEE International Enterprise Distributed Object Computing Conference Workshops (EDOCW) · doi:10.1109/edocw52865.2021.00036 · Cited by 76
Pseudo-Q: Generating Pseudo Language Queries for Visual Grounding
Jiang, H., Lin, Y., Han, D., Song, S., & Huang, G. M. (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/cvpr52688.2022.01507 · Cited by 72
TallyQA: Answering Complex Counting Questions
Acharya, M., Kafle, K., & Kanan, C. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33018076 · Cited by 69
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
Tensorize, Factorize and Regularize: Robust Visual Relationship Learning
Hwang, S. J., Kim, H. J., Ravi, S. N., Collins, M. D., Tao, Z., & Singh, V. (2018) · 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.2018.00112 · Cited by 68
Multimodal feature fusion by relational reasoning and attention for visual question answering
Zhang, W., Yu, J., Hu, H., Hu, H., & Qin, Z. (2019) · Information Fusion · doi:10.1016/j.inffus.2019.08.009 · Cited by 70
LEAF-QA: Locate, Encode & Attend for Figure Question Answering
Chaudhry, R., Shekhar, S., Gupta, U., Maneriker, P., Bansal, P., & Joshi, A. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093269 · Cited by 78
Fake news detection for epidemic emergencies via deep correlations between text and images
Zeng, J., Zhang⋆, Y., & Ma, X. (2020) · Sustainable Cities and Society · doi:10.1016/j.scs.2020.102652 · Cited by 70
Learning Audio-Video Modalities from Image Captions
Nagrani, A., Seo, P. H., Seybold, B., Hauth, A., Manén, S., Sun, C., & Schmid, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19781-9_24 · Cited by 82
Multimodality Representation Learning: A Survey on Evolution, Pretraining and Its Applications
Manzoor, M. A., Albarri, S., Xian, Z., Meng, Z., Nakov, P., & Liang, S. (2023) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3617833 · Cited by 86
Knowledge Graphs for Multi-modal Learning: Survey and Perspective
Chen, Z., Zhang, Y., Fang, Y., Geng, Y., Guo, L., Chen, J., Liu, X., Pan, J. Z., Zhang, N., Chen, H., et al. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103124 · Cited by 79
Nie, L., Wang, W., Hong, R., Wang, M., & Tian, Q. C. (2019) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3343031.3350923 · Cited by 67
Dual self-attention with co-attention networks for visual question answering
Liu, Y., Zhang, X., Zhang, Q., Li, C., Huang, F., Tang, X., & Li, Z. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.107956 · Cited by 68
Local self-attention in transformer for visual question answering
Shen, X., Han, D., Guo, Z., Chen, C., Hua, J., & Luo, G. (2022) · Applied Intelligence · doi:10.1007/s10489-022-04355-w · Cited by 66
Fusion of tactile and visual information in deep learning models for object recognition
Babadian, R. P., Faez, K., Amiri, M., & Falotico, E. (2023) · Information Fusion · doi:10.1016/j.inffus.2022.11.032 · Cited by 67
Textual Context-Aware Dense Captioning With Diverse Words
Shao, Z., Han, J., Debattista, K., & Pang, Y. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3241517 · Cited by 69
Diverse and Coherent Paragraph Generation from Images
Chatterjee, M., & Schwing, A. G. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01216-8_45 · Cited by 67
Temporal Modular Networks for Retrieving Complex Compositional Activities in Videos
Liu, B., Yeung, S. Y., Chou, E., Huang, D.-A., Fei-Fei, L., & Niebles, J. C. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01219-9_34 · Cited by 66
Jointly Discovering Visual Objects and Spoken Words from Raw Sensory Input
Harwath, D. F., Recasens, A., Surís, D., Chuang, G., Torralba, A. B., & Glass, J. R. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01205-0 · Cited by 79
Making History Matter: History-Advantage Sequence Training for Visual Dialog
Yang, T., Zha, Z.-J., & Zhang, H. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00265 · Cited by 73
Multi-Modality Latent Interaction Network for Visual Question Answering
Gao, P., You, H., Zhang, Z., Wang, X., & Li, H. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00592 · Cited by 87
Multimodal feature-wise co-attention method for visual question answering
Zhang, S., Chen, M., Chen, J., Zou, F., Li, Y.-F., & Lü, P. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.02.022 · Cited by 68
Unshuffling Data for Improved Generalization in Visual Question Answering
Teney, D., Abbasnejad, E., & van den Hengel, A. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00145 · Cited by 122
Towards Lightweight Transformer Via Group-Wise Transformation for Vision-and-Language Tasks
Luo, G., Zhou, Y., Sun, X., Wang, Y., Cao, L., Wu, Y., Huang, F., & Ji, R. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3139234 · Cited by 67
Scene Graph Refinement Network for Visual Question Answering
Qian, T., Chen, J., Chen, S., Wu, B., & Jiang, Y. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3169065 · Cited by 69
Chappuis, C., Zermatten, V., Lobry, S., Saux, B. L., & Tuia, D. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00143 · Cited by 74
AMAM: An Attention-based Multimodal Alignment Model for Medical Visual Question Answering
Pan, H., He, S., Zhang, K., Qu, B., Chen, C., & Shi, K. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109763 · Cited by 64
Liu, J., Capurro, D., Nguyen, A., & Verspoor, K. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104466 · Cited by 66
Generative Bias for Robust Visual Question Answering
Cho, J. W., Kim, D.-J., Ryu, H., & Kweon, I. S. (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/cvpr52729.2023.01124 · Cited by 69
Meta Module Network for Compositional Visual Reasoning
Chen, W., Gan, Z., Li, L., Cheng, Y., Wang, W. Y., & Liu, J. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00070 · Cited by 69
Learning to Generalize from Sparse and Underspecified RewardsPreprint
Agarwal, R., Chen, L., Schuurmans, D., & Norouzi, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.07198 · Cited by 66
Phan, L., Gatti, A., Han, Z., Li, N., Hu, J., Zhang, H., Zhang, C. B. C., Shaaban, M., Ling, J., Shi, S., et al. (2025) · arXiv · doi:10.48550/arxiv.2501.14249 · Cited by 65
Self-citationDeepOpht: Medical Report Generation for Retinal Images via Deep Models and Visual Explanation
Huang, J.-H., Yang, C.-H. H., Liu, F., Tian, M., Liu, Y.-C., Wu, T.-W., Lin, I.-H., Wang, K., Morikawa, H., Chang, H.-H., et al. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00249 · Cited by 65
Multimodal Emergent Fake News Detection via Meta Neural Process Networks
Wang, Y., Ma, F., Wang, H., Jha, K., & Gao, J. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467153 · Cited by 67
Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey
Kuang, J., Shen, Y., Xie, J., Luo, H., Xu, Z., Li, R., Li, Y., Cheng, X., Lin, X., & Han, Y. (2025) · ACM Computing Surveys · doi:10.1145/3711680 · Cited by 84
Tell-and-Answer: Towards Explainable Visual Question Answering using Attributes and Captions
Li, Q., Fu, J., Yu, D., Mei, T., & Luo, J. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1164 · Cited by 71
Crowdsourcing in Computer Vision
Kovashka, A., Fei-Fei, L., Russakovsky, O., & Grauman, K. L. (2016) · Foundations and Trends® in Computer Graphics and Vision · doi:10.1561/0600000071 · Cited by 75
AnchorNet: A Weakly Supervised Network to Learn Geometry-Sensitive Features for Semantic Matching
Novotný, D., Larlus, D., & Vedaldi, A. (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.2017.306 · Cited by 65
RePr: Improved Training of Convolutional Filters
Prakash, A., Storer, J. A., Florencio, D. A. F., & Zhang, 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.2019.01092 · Cited by 64
Computer Vision in the Operating Room: Opportunities and Caveats
Kennedy-Metz, L. R., Mascagni, P., Torralba, A. B., DIAS, R. D., Perona, P., Shah, J., Padoy, N., & Zenati, M. A. (2020) · IEEE Transactions on Medical Robotics and Bionics · doi:10.1109/tmrb.2020.3040002 · Cited by 68
Weakly-Supervised Visual-Retriever-Reader for Knowledge-based Question Answering
Luo, M., Zeng, Y., Banerjee, P., & Baral, C. R. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.517 · Cited by 74
Bidirectional generation of structure and properties through a single molecular foundation model
Chang, J., & Ye, J. C. (2024) · Nature Communications · doi:10.1038/s41467-024-46440-3 · Cited by 75
Computer Vision and Natural Language Processing
Wiriyathammabhum, P., Summers-Stay, D. A., Fermuller, C. M., & Aloimonos, Y. (2016) · ACM Computing Surveys · doi:10.1145/3009906 · Cited by 69
Combating Human Trafficking with Multimodal Deep Models
Tong, E., Zadeh, A., Jones, C., & Morency, L. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1142 · Cited by 83
A Unified Framework for Multimodal Domain Adaptation
Qi, F., Yang, X., & Xu, C. (2018) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3240508.3240633 · Cited by 65
Natural Language Guided Visual Relationship Detection
Liao, W., Rosenhahn, B., Ling, S., & Yang, M. Y. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00058 · Cited by 70
History for Visual Dialog: Do we really need it?
Agarwal, S., Bui, T., Lee, J., Konstas, I., & Rieser, V. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.728 · Cited by 70
Liang, Y., Meng, F., Zhang, Y., Chen, Y., Xu, J., & Zhou, J. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i15.17575 · Cited by 63
Socially situated artificial intelligence enables learning from human interaction
Krishna, R., Lee, D., Fei-Fei, L., & Bernstein, M. S. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2115730119 · Cited by 73
Rethinking Data Augmentation for Robust Visual Question Answering
Chen, L., Zheng, Y., & Xiao, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20059-5_6 · Cited by 65
Retrieval Augmented Visual Question Answering with Outside Knowledge
Lin, W., & Byrne, B. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.772 · Cited by 126
Knowledge Enhanced Vision and Language Model for Multi-Modal Fake News Detection
Gao, X., Wang, X., Chen, Z., Zhou, W. J., & Hoi, S. C. H. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3330296 · Cited by 63
Object Referring in Videos with Language and Human Gaze
Vasudevan, A. B., Dai, D., & Gool, L. V. (2018) · 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.2018.00434 · Cited by 67
Removing Bias in Multi-modal Classifiers: Regularization by Maximizing Functional Entropies
Gat, I., Schwartz, I., Schwing, A., & Hazan, T. (2020) · arXiv · doi:10.48550/arxiv.2010.10802 · Cited by 63
Weakly Supervised Semantic Parsing with Abstract Examples
Goldman, O., Latcinnik, V., Naveh, U., Globerson, A., & Berant, J. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1168 · Cited by 64
Violin: A Large-Scale Dataset for Video-and-Language Inference
Liu, J., Chen, W., Cheng, Y., Gan, Z., Yu, L., Yang, Y., & Liu, J. (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/cvpr42600.2020.01091 · Cited by 69
Cascade Reasoning Network for Text-based Visual Question Answering
Liu, F., Xu, G., Wu, Q., Du, Q., Wei, J., & Tan, M. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3413924 · Cited by 62
Metadata
- Authors
- Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., & Parikh, D.
- Affiliations
- Virginia Tech (US)
- Microsoft (United States) (US)
- Georgia Institute of Technology (US)
- Venue
- 2015 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 2425-2433
- OpenAlex
- W1933349210
- Cited by
- 4,487
- References
- 65
- Citations by year
- 2026: 260 · 2025: 519 · 2024: 532 · 2023: 560 · 2022: 518 · 2021: 620 · 2020: 485 · 2019: 432 · 2018: 293 · 2017: 183 · 2016: 76 · 2015: 9
- FWCI
- 76.70
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- visual question answering, open-ended question answering, image understanding, visual reasoning, assistive technology for visually impaired, automated assessment, image captioning
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 ›
Are you an author, or do you see a problem with this page? Request a review
