XGPT: Cross-modal Generative Pre-Training for Image Captioning
Open accessIntegrity signals
Registry
Cites
Statistics
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?
There is a recent surge of research attempts and activities across distinct disciplines exploring Transformer architectures, resulting in many novel multimodal learning methods and significant advances.
“Critically, there is a recent surge of research attempts and activities across distinct disciplines exploring the Transformer architectures, resulting in a large number of novel MML methods being developed in recent years, along with significant and diverse advances in various areas [4], [5], [6], [7], [8].”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [4]
The input to a Transformer can encompass one or multiple sequences of tokens, and each sequence's attribute naturally allows for multimodal learning without architectural modification.
“Concretely, the input to a Transformer could encompass one or multiple sequences of tokens, and each sequence's attribute (e.g., the modality label, the sequential order), naturally allowing for MML without architectural modification [4].”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [4]
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · 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
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
Deep visual-semantic alignments for generating image descriptions
Karpathy, A., & Fei-Fei, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298932 · Cited by 4,902 · see citing works
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
Young, P., Lai, A., Hodosh, M., & Hockenmaier, J. (2014) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00166 · Cited by 2,439 · see citing works
Self-Critical Sequence Training for Image Captioning
Rennie, S. J., Marcheret, E., Mroueh, Y., Ross, J., & Goel, V. (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.2017.131 · Cited by 2,177 · see citing works
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
Lu, J., Batra, D., Parikh, D., & Lee, S. (2019) · arXiv · doi:10.48550/arxiv.1908.02265 · Cited by 2,109 · 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
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
VideoBERT: 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
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 · cited 2× · see citing works
“5 - - - AoANet [24] - - - - 119.”
ReferItGame: Referring to Objects in Photographs of Natural Scenes
Kazemzadeh, S., Ordóñez, V., Matten, M., & Berg, T. L. (2014) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1086 · Cited by 1,082 · see citing works
VQA: Visual Question AnsweringPreprint
Agrawal, A., Lu, J., Antol, S., Mitchell, M., Zitnick, C. L., Batra, D., & Parikh, D. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.00468 · Cited by 968 · see citing works
Exploring 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
HotFlip: White-Box Adversarial Examples for Text Classification
Ebrahimi, J., Rao, A., Lowd, D., & Dou, D. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-2006 · Cited by 948 · 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
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
Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear UnitsPreprint
Hendrycks, D., & Gimpel, K. (2016) · arXiv (Cornell University) · Cited by 753 · see citing works
Using Pre-Training Can Improve Model Robustness and UncertaintyPreprint
Hendrycks, D., Lee, K., & Mazeika, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.09960 · Cited by 531 · see citing works
Lu, J., Yang, J., Batra, D., & Parikh, D. (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.2018.00754 · Cited by 412 · see citing works
Jiang, H., He, P., Chen, W., Liu, X., Gao, J., & Zhao, T. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.197 · Cited by 372 · see citing works
Large-Scale Adversarial Training for Vision-and-Language Representation LearningPreprint
Gan, Z., Chen, Y.-C., Li, L., Zhu, C., Cheng, Y., & Liu, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.06195 · Cited by 343 · see citing works
Robust Neural Machine Translation with Doubly Adversarial Inputs
Cheng, Y., Jiang, L., & Macherey, W. (2019) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1425 · Cited by 280 · see citing works
Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning
Chen, T., Liu, S., Chang, S., Cheng, Y., Amini, L., & Wang, Z. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00078 · Cited by 205 · see citing works
Zhou, L., Kalantidis, Y., Chen, X., Corso, J. J., & 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.2019.00674 · Cited by 191 · see citing works
Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks
Huang, H., Liang, Y., Duan, N., Gong, M., Shou, L., Jiang, D., & Zhou, M. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1252 · Cited by 170 · see citing works
Self-citationContrastive Bidirectional Transformer for Temporal Representation Learning
Sun, C., Baradel, F., Murphy, K. P., & Schmid, C. (2019) · Cited by 109 · see citing works
Flagged first, then by citations
5 of the 35 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Multimodal learning with transformers: A survey
Xu, P., Zhu, X., & Clifton, D. A. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3275156 · Cited by 1,072 · see citing works
Pre-trained models: Past, present and future
Han, X., Zhang, Z., Ding, N., Gu, Y., Liu, X., Huo, Y., Qiu, J., Yao, Y., Zhang, A., Zhang, L., et al. (2021) · AI Open · doi:10.1016/j.aiopen.2021.08.002 · Cited by 994 · 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
From Show to Tell: A Survey on Deep Learning-Based Image Captioning
Stefanini, M., Cornia, M., Baraldi, L., Cascianelli, S., Fiameni, G., & Cucchiara, R. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3148210 · Cited by 373 · see citing works
Large-Scale Adversarial Training for Vision-and-Language Representation LearningPreprint
Gan, Z., Chen, Y.-C., Li, L., Zhu, C., Cheng, Y., & Liu, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.06195 · Cited by 343 · see citing works
VLP: A Survey on Vision-language Pre-training
Chen, F., Zhang, D., Han, M., Chen, X.-Y., Shi, J. C., Xu, S., & Xu, B. (2023) · Machine Intelligence Research · doi:10.1007/s11633-022-1369-5 · Cited by 295 · see citing works
Self-citationUnifying Vision-and-Language Tasks via Text GenerationPreprint
Cho, J., Lei, J., Tan, H. X., & Bansal, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.02779 · Cited by 268 · see citing works
Large-scale Multi-modal Pre-trained Models: A Comprehensive Survey
Wang, X., Chen, G., Qian, G., Gao, P., Wei, X.-Y., Wang, Y., Tian, Y., & Gao, W. (2023) · Machine Intelligence Research · doi:10.1007/s11633-022-1410-8 · Cited by 245 · see citing works
Moon, J. H., Lee, H., Shin, W., Kim, Y., & Choi, E. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3207502 · Cited by 221 · see citing works
Deep Learning Approaches on Image Captioning: A Review
Ghandi, T., Pourreza, H. R., & Mahyar, H. (2023) · ACM Computing Surveys · doi:10.1145/3617592 · Cited by 205 · 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
Valipour, M., Rezagholizadeh, M., Kobyzev, I., & Ghodsi, A. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.239 · Cited by 162 · see citing works
Kaleido-BERT: Vision-Language Pre-training on Fashion Domain
Zhuge, M., Gao, D., Fan, D.-P., Jin, L., Chen, B., Zhou, H., Qiu, M., & Shao, L. (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/cvpr46437.2021.01246 · Cited by 128 · see citing works
Pretrained Language Model for Text Generation: A Survey
Li, J., Tang, T., Zhao, W. X., & Wen, J.-R. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/612 · Cited by 162 · see citing works
Parameter-Efficient Transfer Learning for Remote Sensing Image–Text Retrieval
Yuan, Y., Zhan, Y., & Xiong, Z. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3308969 · Cited by 94 · see citing works
WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-TrainingPreprint
Huo, Y., Zhang, M., Liu, G., Lu, H., Gao, Y., Yang, G., Wen, J., Zhang, H., Xu, B., Zheng, W., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.06561 · Cited by 121 · see citing works
BridgeTower: Building Bridges between Encoders in Vision-Language Representation Learning
Xiao, X., Wu, C., Rosenman, S., Lal, V., Che, W., & Duan, N. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i9.26263 · Cited by 75 · see citing works
Self-citationUniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning
Masry, A., Kavehzadeh, P., Long, X., Hoque, E., & Joty, S. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.906 · Cited by 97 · see citing works
RenAIssance: A Survey Into AI Text-to-Image Generation in the Era of Large Model
Bie, F., Yang, Y., Zhou, Z., Ghanem, A., Zhang, M., Yao, Z., Wu, X., Holmes, C., Golnari, P. A., Clifton, D. A., et al. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3522305 · Cited by 76 · see citing works
NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language Tasks
Sammani, F., Mukherjee, T., & Deligiannis, N. (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/cvpr52688.2022.00814 · Cited by 59 · see citing works
Vision-and-Language Pretrained Models: A Survey
Long, S., Cao, F., Han, S. C., & Yang, H. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/773 · Cited by 46 · see citing works
M3P: Learning Universal Representations via Multitask Multilingual Multimodal Pre-trainingPreprint
Ni, M., Huang, H., Su, L., Cui, E., Bharti, T., Wang, L., Gao, J., Zhang, D., & Duan, N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.02635 · Cited by 40 · see citing works
Self-citationExploring Video Captioning Techniques: A Comprehensive Survey on Deep Learning Methods
Islam, S., Dash, A., Seum, A., Raj, A. H., Hossain, T., & Shah, F. M. (2021) · SN Computer Science · doi:10.1007/s42979-021-00487-x · Cited by 36 · see citing works
RepsNet: Combining Vision with Language for Automated Medical Reports
Tanwani, A. K., Barral, J. K., & Freedman, D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16443-9_68 · Cited by 37 · see citing works
Guo, R., Wei, J., Sun, L., Yu, B., Chang, G., Liu, D., Zhang, S., Yao, Z., Xu, M., & Bu, L. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108709 · Cited by 50 · see citing works
I-Tuning: Tuning Frozen Language Models with Image for Lightweight Image Captioning
Luo, Z., Hu, Z., Xi, Y., Zhang, R., & Ma, J. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096424 · Cited by 36 · see citing works
From Show to Tell: A Survey on Image CaptioningPreprint
Stefanini, M., Cornia, M., Baraldi, L., Cascianelli, S., Fiameni, G., & Cucchiara, R. (2021) · arXiv (Cornell University) · Cited by 31 · see citing works
A survey on knowledge-enhanced multimodal learning
Lymperaiou, M., & Stamou, G. B. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10825-z · Cited by 35 · see citing works
Towards Generating and Evaluating Iconographic Image Captions of Artworks
Cetinić, E. (2021) · Journal of Imaging · doi:10.3390/jimaging7080123 · Cited by 26 · see citing works
Iconographic Image Captioning for Artworks
Cetinić, E. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-68796-0_36 · Cited by 24 · see citing works
COME: Clip-OCR and Master ObjEct for text image captioning
Lv, G., Sun, Y., Nian, F., Zhu, M., Tang, W., & Hu, Z. (2023) · Image and Vision Computing · doi:10.1016/j.imavis.2023.104751 · Cited by 23 · see citing works
Probabilistic Prompt Learning for Dense Prediction
Kwon, H., Song, T., Jeong, S., Kim, J., Jang, J., & Sohn, K. (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/cvpr52729.2023.00654 · Cited by 23 · see citing works
Domain knowledge-driven image captioning for bridge damage description generation
Chai, C., Gao, Y., Xiong, G., Liu, J., & Li, H. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106116 · Cited by 17 · see citing works
Remote Sensing Image-Text Retrieval With Implicit-Explicit Relation Reasoning
Yang, L., Zhou, T., Ma, W., Du, M., Liu, L., Li, F., Zhao, S., & Wang, Y. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3466909 · Cited by 16 · see citing works
Pretrained Language Models for Text Generation: A Survey
Li, J., Tang, T., Zhao, W. X., & Wen, J.-R. (2021) · arXiv · doi:10.48550/arxiv.2105.10311 · Cited by 15 · see citing works
Retrieval Augmented Recipe Generation
Liu, G., Yin, H., Zhu, B., Chen, J., Ngo, C., & Jiang, Y. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00244 · Cited by 15 · see citing works
Reading and Reasoning over Chart Images for Evidence-based Automated Fact-Checking
Akhtar, M., Cocarascu, O., & Simperl, E. (2023) · Findings of the Association for Computational Linguistics: EACL 2023 · doi:10.18653/v1/2023.findings-eacl.30 · Cited by 18 · see citing works
A Survey on Enhancing Image Captioning with Advanced Strategies and Techniques
Zou, B., Thobhani, A., Kui, X., Abdussalam, A., Asim, M., Shah, S., & ELAffendi, M. A. (2025) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2025.059192 · Cited by 15 · see citing works
Multimodal large model pretraining, adaptation and efficiency optimization
Ji, L., Xiao, S., Feng, J., Gao, W., & Zhang, H. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.129138 · Cited by 14 · see citing works
Self-citationModeling Text-visual Mutual Dependency for Multi-modal Dialog GenerationPreprint
Wang, S., Meng, Y., Sun, X., Wu, F. Q., Ouyang, R., Yan, R., Zhang, T., & Li, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.14445 · Cited by 12 · see citing works
A Critical Analysis of Benchmarks, Techniques, and Models in Medical Visual Question Answering
Al-Hadhrami, S. A., Menaï, M. E. B., Al-Ahmadi, S. A., & Alnafessah, A. (2023) · IEEE Access · doi:10.1109/access.2023.3335216 · Cited by 13 · see citing works
Transformers in speech processing: Overcoming challenges and paving the future
Latif, S., Zaidi, S. A. M., Cuayáhuitl, H., Shamshad, F., Shoukat, M., Usama, M., & Qadir, J. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2025.100768 · Cited by 68 · see citing works
A comprehensive survey on deep learning approaches for image captioning: a systematic review
Al-Malla, M. A., Hamdoun, O., & Ghneim, N. (2026) · Journal Of Big Data · doi:10.1186/s40537-026-01377-w · Cited by 11 · see citing works
BigVideo: A Large-scale Video Subtitle Translation Dataset for Multimodal Machine Translation
Kang, L., Huang, L., Peng, N., Zhu, P., Sun, Z., Cheng, S., Wang, M., Huang, D., & Su, J. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.535 · Cited by 10 · see citing works
Cross-modal Semantic Alignment Pre-training for Vision-and-Language Navigation
Wu, S., Fu, X., Wu, F. H., & Zha, Z.-J. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548283 · Cited by 9 · see citing works
InfoMetIC: An Informative Metric for Reference-free Image Caption Evaluation
Hu, A., Chen, S., Zhang, L., & Jin, Q. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.178 · Cited by 13 · see citing works
Multimodal Distillation Pre-Training Model for Ultrasound Dynamic Images Annotation
Chen, X., Jia, K., Zhang, Y., Gou, J., Shen, A., & Wan, S. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3438254 · Cited by 8 · see citing works
NLP-Based Fusion Approach to Robust Image Captioning
Ricci, R., Melgani, F., Marcato, J., & Gonçalves, W. N. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3413323 · Cited by 7 · see citing works
Sentiment Triplet Extraction With Multi-View Contrastive Learning
Wu, W., Wang, D., Wang, M., Feng, S., & Zhang, Y. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2024.3521608 · Cited by 6 · see citing works
Object Detection Meets LLMs: Model Fusion for Safety and Security
Wase, Z. M., Madisetti, V. K., & Bahga, A. (2023) · Journal of Software Engineering and Applications · doi:10.4236/jsea.2023.1612034 · Cited by 6 · see citing works
Visual-Text Reference Pretraining Model for Image Captioning
Li, P., Zhang, M., Lin, P., Wan, J., & Jiang, M. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/9400999 · Cited by 6 · see citing works
Prototype-Guided Multilayer Alignment Network for Few-Shot Object Detection in Remote Sensing
Azeem, A., Li, Z., Siddique, A., Zhang, Y., & Li, Y. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3587389 · Cited by 4 · see citing works
Emotion-Aware Multimodal Pre-training for Image-Grounded Emotional Response Generation
Tian, Z., Wen, Z., Wu, Z., Song, Y., Tang, J., Li, D., & Zhang, N. L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-00129-1_1 · Cited by 4 · see citing works
CIC-BART-SSA: Controllable Image Captioning with Structured Semantic Augmentation
Basioti, K., Abdelsalam, M. A., Fancellu, F., Pavlović, V., & Fazly, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72848-8_26 · Cited by 4 · see citing works
ANONYMOUS (2022) · Research Square · doi:10.21203/rs.3.rs-382748/v3 · Cited by 4 · see citing works
Research on Image Captioning Based on Vision-language Pre-trained Models
Zou, S., Xie, Y., Yan, J., Wei, Y., & Luan, X. (2023) · International Conference on Big Data and Information Analytics (BigDIA) · doi:10.1109/bigdia60676.2023.10429361 · Cited by 3 · see citing works
Comparative Analysis of Fine-Tuned Multimodal Models in Radiology Image Captioning
Hoque, M., Hasan, M. R., Emon, M. I. S., Oluwafemi, E. P. O., Rahman, M. M., & Khalifa, F. (2025) · IEEE International Conference on Computing and Machine Intelligence (ICMI) · doi:10.1109/icmi65310.2025.11141312 · Cited by 3 · see citing works
Prevention of Global Mental Health Crisis with Transformer Neural Networks
Rajagopal, A. R., Nirmala, V., Andrew, J., Arun, M., & Piush, A. (2023) · Intelligent systems reference library · doi:10.1007/978-3-031-12419-8_11 · Cited by 2 · see citing works
Free-FreeSLT: A Gloss-Free Parameter-Free model for Sign Language Translation
Sun, W., Ma, Y., & Wang, R. (2024) · doi:10.1145/3700410.3702115 · Cited by 3 · see citing works
Rajagopal, A., Nirmala, V., Andrew, J., & Arun, M. (2021) · Research Square · doi:10.21203/rs.3.rs-382748/v1 · Cited by 2 · see citing works
Detailed Image Captioning and Hashtag Generation
Shetty, N., & Li, Y. (2024) · Future Internet · doi:10.3390/fi16120444 · Cited by 1 · see citing works
Rajagopal, A. (2022) · Research Square · doi:10.21203/rs.3.rs-382748/v2 · Cited by 1 · see citing works
Vision-and-Language Pretraining: Methods, Applications, and Future ChallengesPreprint
Nguyen, T., Nguyen, C.-D. T., Wu, X., Ng, S.-K., & Luu, A.-T. (2023) · Research Square · doi:10.21203/rs.3.rs-3120051/v1 · Cited by 1 · see citing works
Vision-and-Language Pretraining for VQA
Wu, Q., Wang, P., Wang, X. Y., He, X., & Zhu, W. (2022) · Advances in computer vision and pattern recognition · doi:10.1007/978-981-19-0964-1_6
A Sub-captions Semantic-Guided Network for Image Captioning
Tian, W., Zhu, J., Wu, S., Zhao, Z., Zhang, Y.-Z., & Zhang, T. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-13832-4_31
Tian, Y., Liu, Z., Zou, Q., & Chen, G. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-2390-4_9
Leveraging English Captions for Improved Image Captioning in Low-Resource Languages
Vakada, N., & Chandra, S. C. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-93709-5_3
Analysis and control of online interactions through neural natural language processing
Laugier, L. (2022) · theses.fr (ABES) · doi:10.70675/4743aa15z209az4addz8f29z95150aa230b8
GrowCap: Lightweight Image Captioning with Enhanced Hard Prompts
Tian, H., Ning, B., & Zhou, X. (2026) · Communications in computer and information science · doi:10.1007/978-981-95-7996-9_2
A Multi-Caption Fusion Approach for Enhanced Image CaptioningPreprint
Thobhani, A., Junfeng, Y., Hael, M. A., Abdussalam, A., Asim, M., Ali, G., & Shah, S. (2026) · Research Square · doi:10.21203/rs.3.rs-11237647/v1
- Authors
- Xia, Q., Huang, H., Duan, N., Zhang, D., Ji, L., Sui, Z., Cui, E., Bharti, T., & Zhou, M.
- Affiliations
- Peking University (CN)
- Microsoft Research Asia (China) (CN)
- Corresponding author
- Xia, Qiaolin
- Huang, Haoyang
- Duan, Nan
- Zhang, Dongdong
- Ji, Lei
- Cui, Edward
- Bharti, Taroon
- Zhou, Ming
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2021
- Pages
- 786-797
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3209274285
- Cited by
- 70
- References
- 35
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 12 · 2023: 19 · 2022: 14 · 2021: 12 · 2020: 3
- FWCI
- 4.66
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- image captioning, image retrieval, data augmentation
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
