Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning
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We present a new dataset of image caption annotations, Conceptual Captions, which contains an order of magnitude more images than the MS-COCO dataset (Lin et al., 2014) and represents a wider variety of both images and image caption styles.We achieve this by extracting and filtering image caption annotations from billions of webpages.We also present quantitative evaluations of a number of image captioning models and show that a model architecture based on Inception-ResNet-v2 (Szegedy et al., 2016) for image-feature extraction and Transformer (Vaswani et al., 2017) for sequence modeling achieves the best performance when trained on the Conceptual Captions dataset.
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Several ideas introduced recently include random sampling, learning concept dictionary, uniform masking, patch projection, joint learning of a region detector, and representation aligning before mask prediction.
“Several ideas introduced recently include random sampling [113], learning concept dictionary [203], uniform masking [270], patch projection [192], joint learning of a region detector [271], and representation aligning before mask prediction [263].”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [203]
Recent remedies for fine-grained semantic alignment include random sampling, learning a concept dictionary, jointly learning a region detector, and representation aligning before mask prediction.
“Several remedies introduced recently include random sampling [113], learning concept dictionary [203], jointly learning a region detector [271], and representation aligning before mask prediction [263].”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [203]
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Scheduled Sampling in Vision-Language Pretraining with Decoupled Encoder-Decoder Network
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Zhang, Y., Pan, Y., Zhong, T., Dong, P., Xie, K., Liu, Y., Jiang, H., Wu, Z., Liu, Z., Zhao, W., et al. (2024) · Meta-Radiology · doi:10.1016/j.metrad.2024.100103 · Cited by 52 · see citing works
OpenVLA: An Open-Source Vision-Language-Action Model
Kim, M. J., Pertsch, K., Karamcheti, S., Xiao, T., Balakrishna, A., Nair, S., Rafailov, R., Foster, E., Lam, G., Sanketi, P., et al. (2024) · arXiv · doi:10.48550/arxiv.2406.09246 · Cited by 62 · see citing works
“The Prismatic VLM is fine-tuned on top of these components using the LLaVA 1.5 data mixture [43], which contains a total of approximately 1M image-text and text-only data samples from open-source datasets [29, 42, 81–83].”
MethodInfluentialReinforced Structured State-Evolution for Vision-Language Navigation
Chen, J., Gao, C., Meng, E., Zhang, Q., & Liu, S. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01501 · Cited by 45 · 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
“However, existing large-scale visual language datasets (e.g., Visual Genome [331], Conceptual Cap-tions [332], and LAION-400m [333]) generally suffer from limited size, high annotation noise, insufficient semantic diversity, and expensive annotation.”
Measuring Representational Harms in Image Captioning
Wang, A., Barocas, S., Laird, K., & Wallach, H. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533099 · Cited by 40 · see citing works
Wolfe, R., Yang, Y., Howe, B., & Caliskan, A. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3593013.3594072 · Cited by 42 · see citing works
GilBERT: Generative Vision-Language Pre-Training for Image-Text Retrieval
Hong, W., Ji, K., Liu, J., Wang, J., Chen, J., & Chu, W. (2021) · Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3404835.3462838 · Cited by 37 · see citing works
COOKIE: Contrastive Cross-Modal Knowledge Sharing Pre-training for Vision-Language Representation
Wen, K., Xia, J., Huang, Y., Li, L., Xu, J., & Shao, J. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00221 · Cited by 37 · see citing works
Linearly Mapping from Image to Text Space
Merullo, J., Castricato, L., Eickhoff, C., & Pavlick, E. (2022) · arXiv · doi:10.48550/arxiv.2209.15162 · Cited by 34 · see citing works
Chintalapati, S. S., Bragg, J., & Wang, L. L. (2022) · Proceedings of the 24th International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3517428.3544796 · Cited by 31 · see citing works
Inspecting the Geographical Representativeness of Images from Text-to-Image Models
Basu, A., Babu, R. V., & Pruthi, D. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00474 · Cited by 21 · see citing works
Bridging the Gap between 2D and 3D Visual Question Answering: A Fusion Approach for 3D VQA
Mo, W., & Liu, Y. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i5.28222 · Cited by 20 · see citing works
Prefix Conditioning Unifies Language and Label Supervision
Saito, K., Sohn, K., Zhang, X., Li, C.-L., Lee, C.-Y., Saenko, K., & Pfister, T. (2022) · arXiv · doi:10.48550/arxiv.2206.01125 · Cited by 17 · see citing works
Thinking Fast and Slow: Efficient Text-to-Visual Retrieval with Transformers
Miech, A., Alayrac, J.-B., Laptev, I., Sivic, J., & Zisserman, A. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00970 · Cited by 149 · see citing works
Multi-stage Pre-training over Simplified Multimodal Pre-training Models
Liu, T., Feng, F., & Wang, X. (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.199 · Cited by 11 · see citing works
Vision to Language: Methods, Metrics and Datasets
Sharif, N., Nadeem, U., Shah, S. A. A., Bennamoun, M., & Liu, W. (2020) · Learning and Analytics in Intelligent Systems · doi:10.1007/978-3-030-49724-8_2 · Cited by 9 · see citing works
RAP: Retrieval-Augmented Planning with Contextual Memory for Multimodal LLM Agents
Kagaya, T., Yuan, T. J., Lou, Y., Karlekar, J., Pranata, S., Kinose, A., Oguri, K., Wick, F., & You, Y. (2024) · arXiv · doi:10.48550/arxiv.2402.03610 · Cited by 8 · see citing works
Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models
Karamcheti, S., Nair, S., Balakrishna, A., Liang, P., Kollar, T., & Sadigh, D. (2024) · arXiv · doi:10.48550/arxiv.2402.07865 · Cited by 7
“The first subset consists of a 558K sample mixture of examples sourced from various captioning datasets (e.g., Conceptual Captions, LAION Sharma et al., 2018; Schuhmann et al., 2021), while the second consists of 665K multimodal instruct tuning examples comprised of synthetic data generated in Liu et al. (2023c), as w…”
Quality and Relevance Metrics for Selection of Multimodal Pretraining Data
Rao, R., Rao, S., Nouri, E., Dey, D., Celikyilmaz, A., & Dolan, B. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00486 · Cited by 6 · see citing works
‘We can see a savage’: a case study of the colonial gaze in generative AI algorithms
Alenichev, A., Shaffer, J. D., Kingori, P., Grietens, K. P., Muldoon, J., & Rocher, L. (2026) · AI & SOCIETY · doi:10.1007/s00146-025-02685-0 · Cited by 3 · see citing works
“Combining machine learning techniques in computer vision with natural language processing, image captioning generates a textual description of the most essential information in a given image (Sharma et al. 2018; Ghandi et al. 2024).”
What Matters When Building Vision Language Models for Product Image Analysis?
Trabelsi, A., Zontak, M., Qian, Y., Jackson, B., Khan, S., & Batur, U. (2025) · 2025 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw65960.2025.00151 · Cited by 3 · see citing works
HallE-Control: Controlling Object Hallucination in Large Multimodal Models
Zhai, B., Yang, S., Xu, C., Shen, S., Keutzer, K., Li, C., & Li, M. (2023) · arXiv · doi:10.48550/arxiv.2310.01779 · Cited by 2 · see citing works
Video Diffusion Models: A Survey
Melnik, A., Ljubljanac, M., Lu, C., Yan, Q., Ren, W., & Ritter, H. (2024) · arXiv · doi:10.48550/arxiv.2405.03150 · Cited by 4 · see citing works
IW-Bench: Evaluating Large Multimodal Models for Converting Image-to-Web
Guo, H., Zhang, W., Chen, J., Gu, Y., Yang, J., Du, J., Cao, S., Hui, B., Liu, T., Ma, J., et al. (2024) · arXiv · doi:10.48550/arxiv.2409.18980
“…et al., 2023; Zhu et al., 2023a; Dai et al., 2023; Alayrac et al., 2022; Awadalla et al., 2023; Gao et al., 2023; Google, 2023) that are trained on extensive image-text data, paving the way for multimodal learning (Schuh-mann et al., 2022; Sharma et al., 2018; Lin et al., 2014; Zhu et al., 2023b).”
Enhancing Vision Foundation Models via Multimodal Continual Pre-Training
Chen, Y., Meng, L., Peng, W., Tao, J., Xu, C., Wu, Z., & Jiang, Y.-G. (2025) · arXiv · doi:10.48550/arxiv.2503.18931
- Authors
- Sharma, P., Ding, N., Goodman, S., & Soricut, R.
- Affiliations
- Google (United States) (US)
- Venue
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2018
- Pages
- 2556-2565
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2886641317
- Cited by
- 1,879
- References
- 34
- Citations by year
- 2026: 143 · 2025: 298 · 2024: 369 · 2023: 390 · 2022: 255 · 2021: 250 · 2020: 135 · 2019: 38 · 2018: 1
- FWCI
- 31.53
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- image captioning, InceptionResNetV2, transformer models, image feature extraction, sequence modeling
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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