Vision-Language Pre-Training: Basics, Recent Advances, and Future Trends
Open accessAbstract
This monograph surveys vision-language pre-training (VLP) methods for multimodal intelligence that have been developed in the last few years. We group these approaches into three categories: (i) VLP for image-text tasks, such as image captioning, image-text retrieval, visual question answering, and visual grounding; (ii) VLP for core computer vision tasks, such as (open-set) image classification, object detection, and segmentation; and (iii) VLP for video-text tasks, such as video captioning, video-text retrieval, and video question answering. For each category, we present a comprehensive review of state-of-the-art methods, and discuss the progress that has been made and challenges still being faced, using specific systems and models as case studies. In addition, for each category, we discuss advanced topics being actively explored in the research community, such as big foundation models, unified modeling, in-context few-shot learning, knowledge, robustness, and computer vision in the wild, to name a few.
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During training, specialized encoders extract features from different modalities, then cross-modal alignment mechanisms integrate them.
“During training, the model employs specialized encoders (e.g., convolutional neural networks for visual data, transformer architectures for text and audio data) [171,172] to extract features from different modalities, and then interacts and integrates these features through cross-modal alignment mechanisms [173,174].”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [172]
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 · see citing works
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works
Self-citationGoing deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works
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 · see citing works
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00986 · Cited by 35,534 · see citing works
Self-citationGlove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071 · see citing works
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684 · see citing works
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105 · see citing works
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545 · see citing works
End-to-End Object Detection with Transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_13 · Cited by 18,285 · see citing works
High-Resolution Image Synthesis with Latent Diffusion Models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01042 · Cited by 15,880 · see citing works
Learning Spatiotemporal Features with 3D Convolutional Networks
Tran, D., Bourdev, L., Fergus, R., Torresani, L., & Paluri, M. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.510 · Cited by 9,593 · see citing works
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
Carreira, J., & Zisserman, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.502 · Cited by 9,570 · see citing works
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.703 · Cited by 8,821 · 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
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S. J., & Zhang, L. (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.2018.00636 · Cited by 5,117 · 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
Self-citationDeep 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
VQA: Visual Question Answering
Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., & Parikh, D. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.279 · Cited by 4,487 · see citing works
SlowFast Networks for Video Recognition
Feichtenhofer, C., Fan, H., Malik, J., & He, K. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00630 · Cited by 4,007 · see citing works
Hmdb: a large video database for human motion recognition
Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., & Serre, T. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126543 · Cited by 3,965 · see citing works
GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering
Hudson, D. A., & Manning, C. D. (2019) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2019.00686 · Cited by 3,703 · see citing works
The Power of Scale for Parameter-Efficient Prompt Tuning
Lester, B., Al-Rfou, R., & Constant, N. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.243 · Cited by 3,096 · see citing works
The Power of Scale for Parameter-Efficient Prompt Tuning
Lester, B., Al-Rfou, R., & Constant, N. (2021) · arXiv · doi:10.48550/arxiv.2104.08691 · Cited by 3,092
StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks
Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., & Metaxas, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.629 · Cited by 3,021 · see citing works
ViViT: A Video Vision Transformer
Arnab, A., Dehghani, M., Heigold, G., Sun, C., Lucic, M., & Schmid, C. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00676 · Cited by 2,767 · see citing works
Model Cards for Model Reporting
Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287596 · Cited by 2,681 · see citing works
Edge Boxes: Locating Object Proposals from Edges
Zitnick, C. L., & Dollár, P. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_26 · Cited by 2,656 · see citing works
Taming Transformers for High-Resolution Image Synthesis
Esser, P., Rombach, R., & Ommer, B. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01268 · Cited by 2,514 · see citing works
Film: Visual reasoning with a general conditioning layer
Perez, E., Strub, F., De Vries, H., Dumoulin, V., & Courville, A. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11671 · Cited by 2,490 · see citing works
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé, H., & Crawford, K. (2018) · arXiv · doi:10.48550/arxiv.1803.09010 · Cited by 2,474
Prefix-Tuning: Optimizing Continuous Prompts for Generation
Li, X. L., & Liang, P. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.353 · Cited by 2,473 · see citing works
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., III, H. D., & Crawford, K. (2021) · Communications of the ACM · doi:10.1145/3458723 · Cited by 2,469 · see citing works
Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A., et al. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-020-01316-z · Cited by 2,460 · see citing works
Vision Transformers for Dense Prediction
Ranftl, R., Bochkovskiy, A., & Koltun, V. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01196 · Cited by 2,373 · see citing works
LXMERT: Learning Cross-Modality Encoder Representations from Transformers
Tan, H., & Bansal, M. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1514 · Cited by 2,319 · see citing works
Liu, Z., Ning, J., Cao, Y., Wei, Y., Zhang, Z., Lin, S., & Hu, H. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00320 · Cited by 2,294 · see citing works
Self-citationMaking the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
Goyal, Y., Khot, T., Summers-Stay, D., Batra, D., & Parikh, D. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.670 · Cited by 2,287 · see citing works
UNITER: UNiversal Image-TExt Representation Learning
Chen, Y.-C., Li, L., Yu, L., El Kholy, A., Ahmed, F., Gan, Z., Cheng, Y., & Liu, J. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58577-8_7 · Cited by 2,055 · see citing works
Self-citationA Benchmark Dataset and Evaluation Methodology for Video Object Segmentation
Perazzi, F., Pont-Tuset, J., McWilliams, B., Gool, L. V., Groß, M. H., & Sorkine‐Hornung, A. (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.2016.85 · Cited by 2,039 · see citing works
Stacked Attention Networks for Image Question Answering
Yang, Z., He, X., Gao, J., Deng, L., & Smola, A. J. (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.2016.10 · Cited by 2,036 · see citing works
Self-citationSPICE: Semantic Propositional Image Caption Evaluation
Anderson, P., Fernando, B., Johnson, M., & Gould, S. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46454-1_24 · Cited by 1,986 · see citing works
WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing
Chen, S., Wang, C., Chen, Z., Wu, Y., Liu, S., Chen, Z., Li, J., Kanda, N., Yoshioka, T., Xiao, X., et al. (2022) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2022.3188113 · Cited by 1,926 · see citing works
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
Xu, T., Zhang, P., Huang, Q., Zhang, H., Gan, Z., Huang, X., & He, X. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00143 · Cited by 1,923 · see citing works
Self-citationConditional Prompt Learning for Vision-Language Models
Zhou, K., Yang, J., Loy, C. C., & Liu, Z. (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/cvpr52688.2022.01631 · Cited by 1,918 · see citing works
Self-citationDescribing objects by their attributes
Farhadi, A., Endres, I., Hoiem, D., & Forsyth, D. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206772 · Cited by 1,917 · see citing works
Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning
Sharma, P., Ding, N., Goodman, S., & Soricut, R. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1238 · Cited by 1,879 · see citing works
Image Captioning with Semantic Attention
You, Q., Jin, H., Wang, Z., Chen, F., & Luo, J. (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.2016.503 · Cited by 1,859 · see citing works
MSR-VTT: A Large Video Description Dataset for Bridging Video and Language
Xu, J., Mei, T., Yao, T., & Rui, Y. (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.2016.571 · Cited by 1,810 · see citing works
TinyBERT: Distilling BERT for natural language understanding
Jiao, X., Yin, Y., Shang, L., Jiang, X., Chen, X., Li, L., Wang, F., & Liu, Q. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.372 · Cited by 1,786 · see citing works
Self-citationCLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
Johnson, J., Hariharan, B., van der Maaten, L., Fei-Fei, L., Zitnick, C. L., & Girshick, R. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.215 · Cited by 1,781 · see citing works
Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks
Li, X., Yin, X. P., Li, C., Zhang, P., Hu, X., Zhang, L., Wang, L., Hu, H., Li, D., Wei, F., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58577-8_8 · Cited by 1,736 · see citing works
Self-citationKnowing When to Look: Adaptive Attention via a Visual Sentinel for Image Captioning
Lu, J., Xiong, C., Parikh, D., & Socher, R. (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.2017.345 · Cited by 1,724 · see citing works
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
Fukui, A., Park, D. H., Yang, D., Rohrbach, A., Darrell, T., & Rohrbach, M. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1044 · Cited by 1,582 · see citing works
The “Something Something” Video Database for Learning and Evaluating Visual Common Sense
Goyal, R., Kahou, S. E., Michalski, V., Materzynska, J., Westphal, S., Kim, H., Haenel, V., Fruend, I., Yianilos, P., Mueller-Freitag, M., et al. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.622 · Cited by 1,471 · see citing works
Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models
Plummer, B. A., Wang, L., Cervantes, C. M., Caicedo, J. C., Hockenmaier, J., & Lazebnik, S. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.303 · Cited by 1,444 · see citing works
Self-citationStacked Cross Attention for Image-Text Matching
Lee, K.-H., Chen, X., Hua, G., Hu, H., & He, X. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01225-0_13 · Cited by 1,402 · 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
Self-citationDense-Captioning Events in Videos
Krishna, R., Hata, K., Ren, F., Fei-Fei, L., & Niebles, J. C. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.83 · Cited by 1,279 · see citing works
LVIS: A Dataset for Large Vocabulary Instance Segmentation
Gupta, A., Dollar, P., & Girshick, R. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00550 · Cited by 1,274 · see citing works
Meshed-Memory Transformer for Image Captioning
Cornia, M., Stefanini, M., Baraldi, L., & Cucchiara, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01059 · Cited by 1,259 · see citing works
Every Picture Tells a Story: Generating Sentences from Images
Farhadi, A., Hejrati, M., Sadeghi, M. A., Young, P., Rashtchian, C., Hockenmaier, J., & Forsyth, D. A. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15561-1_2 · Cited by 1,240 · see citing works
Multi-Task Deep Neural Networks for Natural Language Understanding
Liu, X., He, P., Chen, W., & Gao, J. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1441 · Cited by 1,216 · see citing works
Self-citationVideoBERT: A Joint Model for Video and Language Representation Learning
Sun, C., Myers, A., Vondrick, C., Murphy, K. P., & Schmid, C. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00756 · Cited by 1,212 · 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
Anderson, P., Wu, Q., Teney, D., Bruce, J., Johnson, M., Sunderhauf, N., Reid, I., Gould, S., & van den Hengel, A. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00387 · Cited by 1,187 · see citing works
Attention on Attention for Image Captioning
Huang, L., Wang, W., Chen, J., & Wei, X.-Y. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00473 · Cited by 1,092 · see citing works
(2016) · doi:10.1007/978-3-319-46475-6_5 · Cited by 1,081 · see citing works
Deep Modular Co-Attention Networks for Visual Question Answering
Zhou, Y., Yu, J., Cui, Y., Tao, D., & Tian, Q. 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.2019.00644 · Cited by 1,013 · see citing works
HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips
Miech, A., Zhukov, D., Alayrac, J.-B., Tapaswi, M., Laptev, I., & Sivic, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00272 · Cited by 986 · see citing works
Localizing Moments in Video with Natural Language
Hendricks, L. A., Wang, O., Shechtman, E., Šivic, J., Darrell, T., & Russell, B. C. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.618 · Cited by 983 · see citing works
Flamingo: a visual language model for few-shot learning
Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-1723 · Cited by 966 · see citing works
VinVL: Revisiting Visual Representations in Vision-Language Models
Zhang, P., Li, X., Hu, X., Yang, J., Zhang, L., Wang, L., Choi, Y., & Gao, J. (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/cvpr46437.2021.00553 · Cited by 965 · see citing works
Self-citationExploring Visual Relationship for Image Captioning
Yao, T., Pan, Y., Li, Y., & Mei, T. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01264-9_42 · Cited by 962 · see citing works
Andreas, J., Rohrbach, M., Darrell, T., & Klein, D. (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.2016.12 · Cited by 933 · see citing works
SUN attribute database: Discovering, annotating, and recognizing scene attributes
Patterson, G., & Hays, J. H. (2012) · 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.2012.6247998 · Cited by 878 · see citing works
Unified Vision-Language Pre-Training for Image Captioning and VQA
Zhou, L., Palangi, H., Zhang, L., Hu, H., Corso, J., & Gao, J. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.7005 · Cited by 864 · see citing works
Self-citationMAttNet: Modular Attention Network for Referring Expression Comprehension
Yu, L., Lin, Z., Shen, X., Yang, J., Lu, X., Bansal, M., & Berg, T. L. (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.2018.00142 · Cited by 857 · see citing works
Auto-Encoding Scene Graphs for Image Captioning
Yang, X., Tang, K., Zhang, H., & Cai, J. (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.2019.01094 · Cited by 852 · see citing works
BabyTalk: Understanding and Generating Simple Image Descriptions
Kulkarni, G. A., Premraj, V., Ordóñez, V., Dhar, S., Li, S., Choi, Y., Berg, A. C., & Berg, T. L. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.162 · Cited by 839 · see citing works
Multi-modal Factorized Bilinear Pooling with Co-attention Learning for Visual Question Answering
Zhou, Y., Yu, J., Jianping Fan, B., & Tao, D. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.202 · Cited by 796 · see citing works
Unicoder-VL: A Universal Encoder for Vision and Language by Cross-Modal Pre-Training
Li, G., Duan, N., Fang, Y., Gong, M., & Jiang, D. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6795 · Cited by 795 · see citing works
Visual7W: Grounded Question Answering in Images
Zhu, Y., Groth, O., Bernstein, M., & Fei-Fei, L. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.540 · Cited by 794 · see citing works
Grounded Compositional Semantics for Finding and Describing Images with Sentences
Socher, R., Karpathy, A., Le, Q. V., Manning, C. D., & Ng, A. Y. (2014) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00177 · Cited by 779 · see citing works
Towards VQA Models That Can Read
Singh, A., Natarajan, V., Shah, M., Jiang, Y. L., Chen, X., Batra, D., Parikh, D., & Rohrbach, M. (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.2019.00851 · Cited by 777 · 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
Patient Knowledge Distillation for BERT Model Compression
Sun, S., Cheng, Y., Gan, Z., & Liu, J. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1441 · Cited by 758 · see citing works
Self-citationBoosting Image Captioning with Attributes
Yao, T., Pan, Y., Li, Y., Qiu, Z., & Mei, T. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.524 · Cited by 752 · see citing works
Dual Attention Networks for Multimodal Reasoning and Matching
Nam, H., Ha, J.-W., & Kim, J. (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.2017.232 · Cited by 749 · see citing works
OCR-VQA: Visual Question Answering by Reading Text in Images
Mishra, A., Shekhar, S., Singh, A., & Chakraborty, A. (2019) · IEEE International Conference on Document Analysis and Recognition · doi:10.1109/icdar.2019.00156 · Cited by 746 · see citing works
High-Resolution Image Synthesis with Latent Diffusion Models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2021) · arXiv · doi:10.48550/arxiv.2112.10752 · Cited by 737 · see citing works
MUTAN: Multimodal Tucker Fusion for Visual Question Answering
Ben-younes, H., Cadène, R., Cord, M., & Thome, N. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.285 · Cited by 733 · see citing works
X-Linear Attention Networks for Image Captioning
Pan, Y., Yao, T., Li, Y., & Mei, T. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01098 · Cited by 720 · see citing works
Vector Quantized Diffusion Model for Text-to-Image Synthesis
Gu, S., Chen, D., Bao, J., Wen, F., Zhang, B., Chen, D., Yuan, L., & Guo, B. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01043 · Cited by 720 · see citing works
Objects365: A Large-Scale, High-Quality Dataset for Object Detection
Shao, S., Li, Z., Zhang, T., Peng, C., Yu, G., Zhang, X., Li, J., & Sun, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00852 · Cited by 673 · see citing works
Towards Automatic Learning of Procedures From Web Instructional Videos
Zhou, L., Xu, C., & Corso, J. J. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12342 · Cited by 663 · see citing works
Adversarial NLI: A New Benchmark for Natural Language Understanding
Nie, Y., Williams, A., Dinan, E., Bansal, M., Weston, J., & Kiela, D. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.441 · Cited by 656 · see citing works
MirrorGAN: Learning Text-To-Image Generation by Redescription
Qiao, T., Zhang, J., Xu, D., & Tao, D. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00160 · Cited by 644 · see citing works
Ask Your Neurons: A Neural-Based Approach to Answering Questions about Images
Malinowski, M., Rohrbach, M., & Fritz, M. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.9 · Cited by 633 · see citing works
Visual Semantic Reasoning for Image-Text Matching
Li, K., Zhang, Y., Li, K., Li, Y., & Fu, Y. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00475 · Cited by 628 · see citing works
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts
Changpinyo, S., Sharma, P. K., Ding, N., & Soricut, 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/cvpr46437.2021.00356 · Cited by 626 · see citing works
RegionCLIP: Region-based Language-Image Pretraining
Zhong, Y., Yang, J., Zhang, P., Li, C., Codella, N., Li, L. H., Zhou, L., Dai, X., Yuan, L., Li, Y., et al. (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/cvpr52688.2022.01629 · Cited by 623 · see citing works
Self-citationDual-path Convolutional Image-Text Embeddings with Instance Loss
Zheng, Z., Zheng, L., Garrett, M., Yang, Y., Xu, M., & Shen, Y.-D. (2020) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3383184 · Cited by 622 · see citing works
End-to-End Learning of Visual Representations From Uncurated Instructional Videos
Miech, A., Alayrac, J.-B., Smaira, L., Laptev, I., Šivic, J., & Zisserman, A. (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/cvpr42600.2020.00990 · Cited by 622 · see citing works
VizWiz Grand Challenge: Answering Visual Questions from Blind People
Gurari, D., Li, Q., Stangl, A. J., Guo, A., Lin, C., Grauman, K., Luo, J., & Bigham, J. P. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00380 · Cited by 621 · see citing works
Multi-modal Transformer for Video Retrieval
Gabeur, V., Sun, C., Alahari, K., & Schmid, C. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58548-8_13 · Cited by 615 · see citing works
Less is More: CLIPBERT for Video-and-Language Learning via Sparse Sampling
Lei, J., Li, L., Zhou, L., Gan, Z., Berg, T. L., Bansal, M., & Liu, J. (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/cvpr46437.2021.00725 · Cited by 606 · see citing works
Self-citationFLAVA: A Foundational Language And Vision Alignment Model
Singh, A., Hu, R., Goswami, V., Couairon, G., Galuba, W., Rohrbach, M., & Kiela, D. (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/cvpr52688.2022.01519 · Cited by 597 · see citing works
Image Segmentation Using Text and Image Prompts
Lüddecke, T., & Ecker, A. S. (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/cvpr52688.2022.00695 · Cited by 592 · see citing works
Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering
Agrawal, A., Batra, D., Parikh, D., & Kembhavi, A. (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.2018.00522 · Cited by 586 · see citing works
Detecting Twenty-Thousand Classes Using Image-Level Supervision
Zhou, X., Girdhar, R., Joulin, A., Krähenbühl, P., & Misra, I. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-20077-9_21 · Cited by 564 · see citing works
TVQA: Localized, Compositional Video Question Answering
Lei, J., Yu, L., Bansal, M., & Berg, T. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1167 · Cited by 541 · see citing works
Learning to Reason: End-to-End Module Networks for Visual Question Answering
Hu, R., Andreas, J., Rohrbach, M., Darrell, T., & Saenko, K. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.93 · Cited by 523 · see citing works
Extract Free Dense Labels from CLIP
Zhou, C., Loy, C. C., & Dai, B. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-19815-1_40 · Cited by 513 · see citing works
Multimodal intelligence: Representation learning, information fusion, and applications
Zhang, C., Yang, Z., He, X., & Deng, L. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.2987728 · Cited by 511 · see citing works
VaTeX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research
Wang, X., Wu, J., Chen, J., Li, L., Wang, Y., & Wang, W. Y. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00468 · Cited by 506 · see citing works
Self-citationWhere to Look: Focus Regions for Visual Question Answering
Shih, K. J., Singh, S., & Hoiem, D. (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.2016.499 · Cited by 503 · see citing works
TGIF-QA: Toward Spatio-Temporal Reasoning in Visual Question Answering
Jang, Y., Song, Y., Yu, Y., Kim, Y., & Kim, 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.2017.149 · Cited by 496 · see citing works
Inferring and Executing Programs for Visual Reasoning
Johnson, J., Hariharan, B., van der Maaten, L., Hoffman, J., Fei-Fei, L., Zitnick, C. L., & Girshick, R. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.325 · Cited by 487 · see citing works
A Corpus for Reasoning about Natural Language Grounded in Photographs
Suhr, A., Zhou, S., Zhang, A. Q., Zhang, I., Bai, H., & Artzi, Y. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1644 · Cited by 487 · see citing works
GroupViT: Semantic Segmentation Emerges from Text Supervision
Xu, J., Mello, S. D., Liu, S., Byeon, W., Breuel, T. M., Kautz, J., & Wang, X. (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/cvpr52688.2022.01760 · Cited by 475 · see citing works
Grounding Action Descriptions in Videos
Regneri, M., Rohrbach, M., Wetzel, D., Thater, S., Schiele, B., & Pinkal, M. (2013) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00207 · Cited by 471 · see citing works
VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding
Xu, H., Ghosh, G., Huang, P.-Y., Okhonko, D., Aghajanyan, A., Metze, F., Zettlemoyer, L., & Feichtenhofer, C. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.544 · Cited by 468 · see citing works
Graph-Structured Representations for Visual Question Answering
Teney, D., Liu, L., & van den Hengel, 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.2017.344 · Cited by 464 · see citing works
A dataset for Movie Description
Rohrbach, A., Rohrbach, M., Tandon, N., & Schiele, B. (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.2015.7298940 · Cited by 450 · see citing works
Modeling Context Between Objects for Referring Expression Understanding
Nagaraja, V., Morariu, V. I., & Davis, L. S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46493-0_48 · Cited by 450 · 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
Das, A., Kottur, S., Gupta, K., Singh, A., Yadav, D., Moura, J. M. F., Parikh, D., & Batra, D. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.121 · Cited by 445 · see citing works
Video Question Answering via Gradually Refined Attention over Appearance and Motion
Xu, D., Zhao, Z., Xiao, J., Wu, F., Zhang, H., He, X., & Zhuang, Y. (2017) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3123266.3123427 · Cited by 438 · see citing works
A Fast and Accurate One-Stage Approach to Visual Grounding
Yang, Z., Gong, B., Wang, L., Huang, W., Yu, D., & Luo, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00478 · Cited by 431 · see citing works
Self-citationRobust fine-tuning of zero-shot models
Wortsman, M., Ilharco, G., Kim, J. W., Li, M., Kornblith, S., Roelofs, R., Lopes, R. G., Hajishirzi, H., Farhadi, A., Namkoong, H., et al. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00780 · Cited by 428 · see citing works
Convolutional Image Captioning
Aneja, J., Deshpande, A., & Schwing, A. G. (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.2018.00583 · Cited by 426 · see citing works
Prompting Visual-Language Models for Efficient Video Understanding
Ju, C., Han, T., Zheng, K., Zhang, Y., & Xie, W. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19833-5_7 · Cited by 423 · see citing works
Dynamic Fusion With Intra- and Inter-Modality Attention Flow for Visual Question Answering
Gao, P., Jiang, Z., You, H., Lu, P., Hoi, S. C. H., Wang, X., & Li, H. (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.2019.00680 · Cited by 415 · see citing works
ActBERT: Learning Global-Local Video-Text Representations
Zhu, L., & Yang, 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/cvpr42600.2020.00877 · Cited by 414 · see citing works
HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training
Li, L., Chen, Y.-C., Cheng, Y., Gan, Z., Yu, L., & Liu, J. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.161 · Cited by 408 · see citing works
Self-citationEntangled Transformer for Image Captioning
Li, G., Zhu, L., Liu, P., & Yang, Y. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00902 · Cited by 406 · see citing works
TransVG: End-to-End Visual Grounding with Transformers
Deng, J., Yang, Z., Chen, T., Zhou, W., & Li, H. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00179 · Cited by 406 · see citing works
LiT: Zero-Shot Transfer with Locked-image text Tuning
Zhai, X., Wang, X., Mustafa, B., Steiner, A., Keysers, D., Kolesnikov, A., & Beyer, L. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01759 · Cited by 402 · see citing works
Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge
Teney, D., Anderson, P., He, X., & van den Hengel, A. (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.2018.00444 · Cited by 398 · see citing works
Relation-Aware Graph Attention Network for Visual Question Answering
Li, L., Gan, Z., Cheng, Y., & Liu, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.01041 · Cited by 398 · see citing works
Self-citationnocaps: novel object captioning at scale
Agrawal, H., Desai, K., Wang, Y., Chen, X., Jain, R., Johnson, M., Batra, D., Parikh, D., Lee, S., & Anderson, P. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00904 · Cited by 386 · see citing works
Similarity Reasoning and Filtration for Image-Text Matching
Diao, H., Zhang, Y., Ma, L., & Lu, H. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i2.16209 · Cited by 384 · see citing works
In Defense of Grid Features for Visual Question Answering
Jiang, H., Misra, I., Rohrbach, M., Miller, E. G., & Chen, X. (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/cvpr42600.2020.01028 · Cited by 379 · see citing works
A Hierarchical Approach for Generating Descriptive Image Paragraphs
Krause, J., Johnson, J., Krishna, R., & Fei-Fei, L. (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.2017.356 · Cited by 374 · see citing works
An Empirical Study of Training End-to-End Vision-and-Language Transformers
Dou, Z.-Y., Xu, Y., Gan, Z., Wang, J., Wang, S., Wang, L., Zhu, C., Zhang, P., Yuan, L., Peng, N., et al. (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/cvpr52688.2022.01763 · Cited by 366 · see citing works
Self-citationExpanding Language-Image Pretrained Models for General Video Recognition
Ni, B., Peng, H., Chen, M., Zhang, S., Meng, G., Fu, J., Xiang, S., & Ling, H. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19772-7_1 · Cited by 361 · see citing works
Nguyen, D.-K., & Okatani, T. (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.2018.00637 · Cited by 356 · see citing works
A Joint Sequence Fusion Model for Video Question Answering and Retrieval
Yu, Y., Kim, J.-S., & Kim, G. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01234-2_29 · Cited by 347 · see citing works
Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors
Gafni, O., Polyak, A., Ashual, O., Sheynin, S., Parikh, D., & Taigman, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19784-0_6 · Cited by 345 · see citing works
Dual-level Collaborative Transformer for Image Captioning
Luo, Y., Ji, J., Sun, X., Cao, L., Wu, Y., Huang, F., Lin, C.-W., & Ji, R. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i3.16328 · Cited by 343 · see citing works
ERNIE-ViL: Knowledge Enhanced Vision-Language Representations through Scene Graphs
Yu, F. R., Tang, J., Yin, W., Sun, Y., Tian, H., Wu, H. R., & Wang, H. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i4.16431 · Cited by 334 · see citing works
An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA
Yang, Z., Gan, Z., Wang, J., Hu, X., Lu, Y., Liu, Z., & Wang, L. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i3.20215 · Cited by 334 · see citing works
Self-citationMUREL: Multimodal Relational Reasoning for Visual Question Answering
Cadene, R., Ben-younes, H., Cord, M., & Thome, N. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00209 · Cited by 330 · see citing works
VL-ADAPTER: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks
Sung, Y.-L., Cho, J., & Bansal, M. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00516 · Cited by 329 · see citing works
NExT-QA: Next Phase of Question-Answering to Explaining Temporal Actions
Xiao, J., Shang, X., Yao, A., & Chua, T. (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/cvpr46437.2021.00965 · Cited by 315 · see citing works
Scene Text Visual Question Answering
Biten, A. F., Tito, R., Mafla, A., Gómez, L., Rusiñol, M., Jawahar, C. V., Valveny, E., & Karatzas, D. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00439 · Cited by 311 · see citing works
Evaluating knowledge transfer and zero-shot learning in a large-scale setting
Rohrbach, M., Stark, M. M., & Schiele, B. (2011) · doi:10.1109/cvpr.2011.5995627 · Cited by 309 · see citing works
Vision-Language Pre-Training with Triple Contrastive Learning
Yang, J., Duan, J., Tran, S., Xu, Y., Chanda, S., Chen, L., Zeng, B., Chilimbi, T., & Huang, J. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01522 · Cited by 309 · see citing works
UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning
Li, W., Gao, C., Niu, G., Xiao, X., Liu, H., Liu, J., Wu, H. R., & Wang, H. (2021) · doi:10.18653/v1/2021.acl-long.202 · Cited by 308 · see citing works
Graph Structured Network for Image-Text Matching
Liu, C., Mao, Z., Zhang, T., Xie, H., Wang, B., & Zhang, 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/cvpr42600.2020.01093 · Cited by 305 · see citing works
Context-Aware Attention Network for Image-Text Retrieval
Zhang, Q., Lei, Z., Zhang, Z., & Li, S. Z. (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/cvpr42600.2020.00359 · Cited by 304 · see citing works
UniT: Multimodal Multitask Learning with a Unified Transformer
Hu, R., & Singh, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00147 · Cited by 303 · see citing works
A-OKVQA: A Benchmark for Visual Question Answering Using World Knowledge
Schwenk, D., Khandelwal, A., Clark, C., Marino, K., & Mottaghi, R. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20074-8_9 · Cited by 295 · see citing works
Associating neural word embeddings with deep image representations using Fisher Vectors
Klein, B., Lev, G., Sadeh, G., & Wolf, L. (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.2015.7299073 · Cited by 290 · see citing works
ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering
Yu, Z., Xu, D., Yu, J., Yu, T., Zhao, Z., Zhuang, Y., & Tao, D. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33019127 · Cited by 281 · see citing works
TVR: A Large-Scale Dataset for Video-Subtitle Moment Retrieval
Lei, J., Yu, L., Berg, T. L., & Bansal, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58589-1_27 · Cited by 275 · see citing works
COIN: A Large-Scale Dataset for Comprehensive Instructional Video Analysis
Tang, Y., Ding, D., Rao, Y., Zheng, Y., Zhang, D., Zhao, L., Lu, J., & Zhou, J. (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.2019.00130 · Cited by 270 · see citing works
Improving One-Stage Visual Grounding by Recursive Sub-query Construction
Yang, Z., Chen, T., Wang, L., & Luo, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58568-6_23 · Cited by 270 · see citing works
Self-citationMultiMAE: Multi-modal Multi-task Masked Autoencoders
Bachmann, R., Mizrahi, D., Atanov, A., & Zamir, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19836-6_20 · Cited by 268 · see citing works
VLN↻BERT: A Recurrent Vision-and-Language BERT for Navigation
Hong, Y., Wu, Q., Qi, Y., Rodriguez-Opazo, C., & Gould, S. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00169 · Cited by 264 · see citing works
Learning to Answer Questions from Image Using Convolutional Neural Network
Ma, L., Lu, Z., & Li, H. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10442 · Cited by 260 · see citing works
Huang, T.-H., Ferraro, F., Mostafazadeh, N., Misra, I., Agrawal, A., Devlin, J., Girshick, R., He, X., Kohli, P., Batra, D., et al. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1147 · Cited by 254 · see citing works
Grounding Referring Expressions in Images by Variational Context
Zhang, H., Niu, Y., & Chang, S. (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.2018.00437 · Cited by 247 · see citing works
TextCaps: A Dataset for Image Captioning with Reading Comprehension
Sidorov, O., Hu, R., Rohrbach, M., & Singh, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58536-5_44 · Cited by 242 · see citing works
Explicit Knowledge-based Reasoning for Visual Question Answering
Wang, P., Wu, Q., Shen, C., Dick, A., & van den Hengel, A. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/179 · Cited by 240 · see citing works
Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionality
Thrush, T., Jiang, R., Bartolo, M., Singh, A., Williams, A., Kiela, D., & Ross, C. (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/cvpr52688.2022.00517 · Cited by 240 · see citing works
Instance-Aware Image and Sentence Matching with Selective Multimodal LSTM
Huang, Y., Wang, W., & Wang, L. (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.2017.767 · Cited by 237 · see citing works
Self-citationRevisiting Visual Question Answering Baselines
Jabri, A., Joulin, A., & van der Maaten, L. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46484-8_44 · Cited by 235 · see citing works
A Real-Time Cross-Modality Correlation Filtering Method for Referring Expression Comprehension
Liao, Y., Liu, S., Li, G., Wang, F., Chen, Y., Qian, C., & Li, B. 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/cvpr42600.2020.01089 · Cited by 235 · see citing works
Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning
Huang, Z., Zeng, Z., Huang, Y., Liu, B., Fu, D., & Fu, J. (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/cvpr46437.2021.01278 · Cited by 231 · see citing works
Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI
Pushkarna, M., Zaldivar, A., & Kjartansson, O. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533231 · Cited by 231 · see citing works
A Survey of Vision-Language Pre-Trained Models
Du, Y., Liu, Z., Li, J., & Zhao, W. X. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/762 · Cited by 225 · see citing works
Self-citationJust Ask: Learning to Answer Questions from Millions of Narrated Videos
Yang, A., Miech, A., Šivic, J., Laptev, I., & Schmid, C. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00171 · Cited by 220 · see citing works
Hierarchy Parsing for Image Captioning
Yao, T., Pan, Y., Li, Y., & Mei, T. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00271 · Cited by 219 · see citing works
Vision-and-Language Navigation: A Survey of Tasks, Methods, and Future Directions
Gu, J., Stefani, E., Wu, Q., Thomason, J., & Wang, X. (2022) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2022.acl-long.524 · Cited by 216 · see citing works
WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning
Srinivasan, K., Raman, K., Chen, J., Bendersky, M., & Najork, M. (2021) · Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3404835.3463257 · Cited by 214 · see citing works
Cross-Task Weakly Supervised Learning From Instructional Videos
Zhukov, D., Alayrac, J.-B., Cinbiş, R. G., Fouhey, D. F., Laptev, I., & Šivic, J. (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.2019.00365 · Cited by 208 · see citing works
Position Focused Attention Network for Image-Text Matching
Wang, Y., Yang, H., Qian, X., Ma, L., Lu, J. J., Li, B., & Fan, X. N. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/526 · Cited by 208 · see citing works
Liu, C., Mao, Z., Liu, A.-A., Zhang, T., Wang, B., & Zhang, Y. (2019) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3343031.3350869 · Cited by 206 · see citing works
KRISP: Integrating Implicit and Symbolic Knowledge for Open-Domain Knowledge-Based VQA
Marino, K., Chen, X., Parikh, D., Gupta, A., & Rohrbach, M. (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/cvpr46437.2021.01389 · Cited by 204 · see citing works
TVQA+: Spatio-Temporal Grounding for Video Question Answering
Lei, J., Yu, L., Berg, T. L., & Bansal, M. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.730 · Cited by 197 · see citing works
Captioning Images Taken by People Who Are Blind
Gurari, D., Zhao, Y., Zhang, M., & Bhattacharya, N. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58520-4_25 · Cited by 182 · see citing works
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections
Li, C., Xu, H., Tian, J., Wang, W., Yan, M., Bi, B., Ye, J., Chen, H., Xu, G., Cao, Z., et al. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.488 · Cited by 179 · see citing works
Self-citationAlign and Prompt: Video-and-Language Pre-training with Entity Prompts
Li, D., Li, J., Li, H., Niebles, J. C., & Hoi, S. C. H. (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/cvpr52688.2022.00490 · Cited by 178 · see citing works
Connecting Vision and Language with Localized Narratives
Pont-Tuset, J., Uijlings, J., Changpinyo, S., Soricut, R., & Ferrari, V. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58558-7_38 · Cited by 176 · see citing works
Explainable Neural Computation via Stack Neural Module Networks
Hu, R., Andreas, J., Darrell, T., & Saenko, K. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01234-2_4 · Cited by 175 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
RemoteCLIP: A Vision Language Foundation Model for Remote Sensing
Liu, F., Chen, D., Guan, Z., Zhou, X., Zhu, J., Ye, Q., Fu, L., & Zhou, J. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3390838 · Cited by 507 · see citing works
Foundation Models Defining a New Era in Vision: A Survey and Outlook
Awais, M., Naseer, M., Khan, S., Anwer, R. M., Cholakkal, H., Shah, M., Yang, M.-H., & Khan, F. S. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3506283 · Cited by 304 · see citing works
RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
Brohan, A., Brown, N., Carbajal, J., Chebotar, Y., Chen, X., Choromanski, K., Ding, T., Driess, D., Dubey, A., Finn, C., et al. (2023) · arXiv · doi:10.48550/arxiv.2307.15818 · Cited by 276 · see citing works
Multimodal fusion and vision–language models: A survey for robot vision
Han, X., Chen, S., Fu, Z., Feng, Z., Fan, L., An, D., Wang, C., Guo, L., Meng, W., Zhang, X., et al. (2026) · Information Fusion · doi:10.1016/j.inffus.2025.103652 · Cited by 41 · see citing works
Kumar, A., Baijal, A., & Kharat, M. (2025) · Journal of International Education in Business · doi:10.1108/jieb-07-2024-0086 · Cited by 6 · see citing works
- Authors
- Gan, Z., Li, L., Li, C., Wang, L., Liu, Z., & Gao, J.
- Affiliations
- Microsoft (United States) (US)
- Venue
- Foundations and Trends® in Computer Graphics and Vision
- Publisher
- Emerald
- Published
- 2022-12-05
- Volume
- 14
- Issue
- 3-4
- Pages
- 163-352
- ISSN
- 1572-2740 (ISSN-L)
- 1572-2759
- OpenAlex
- W4312810944
- Cited by
- 155
- References
- 291
- Citations by year
- 2026: 20 · 2025: 54 · 2024: 49 · 2023: 32
- FWCI
- 11.05
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Foundations and Trends® in Computer Graphics and Vision (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- vision-language pre-training, multimodal learning, image captioning, image-text retrieval, visual question answering, visual grounding, video captioning, video-text retrieval, video question answering, image classification, object detection, semantic segmentation, foundation models, unified modeling, robustness
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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