---
title: "Who cites An Image is Worth 16x16 Words: Transformers for…"
description: "1947 papers of the Elamur Research library cite «An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale», found in their…"
canonical: "https://research.elamur.ai/free/en/papers/dosovitskiy-2020-image-worth-16x16-words-transformers/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/dosovitskiy-2020-image-worth-16x16-words-transformers/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [An Image is Worth 16x16 Words: Transformers for…](https://md.research.elamur.ai/free/en/papers/dosovitskiy-2020-image-worth-16x16-words-transformers/) / cited by

# Who cites «An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale»

1947 papers of our library cite it (of the 140,884 whose bibliographies we read) · 78 with a page here, the most cited first · [← the paper](https://md.research.elamur.ai/free/en/papers/dosovitskiy-2020-image-worth-16x16-words-transformers/)

1. [**Swin Transformer: Hierarchical Vision Transformer using Shifted Windows**Ze Liu, Yutong Lin, Yue Cao, et al.*2021 · 33,826 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-swin-transformer-hierarchical-vision-transformer/)
2. [**Learning Transferable Visual Models From Natural Language Supervision**Alec Radford, Jong Wook Kim, Chris Hallacy, et al.*2021 · 8,295 citations*](https://md.research.elamur.ai/free/en/papers/radford-2021-learning-transferable-visual-models-natural/)
3. [**Survey of Hallucination in Natural Language Generation**Ziwei Ji, Nayeon Lee, Rita Frieske, et al.*2022 · 5,947 citations*](https://md.research.elamur.ai/free/en/papers/ji-2022-survey-hallucination-natural-language-generation/)
4. [**Emerging Properties in Self-Supervised Vision Transformers**Mathilde Caron, Hugo Touvron, Ishan Misra, et al.*2021 · 5,910 citations*](https://md.research.elamur.ai/free/en/papers/caron-2021-emerging-properties-self-supervised-vision/)
5. [**On the Opportunities and Risks of Foundation Models**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,044 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/)
6. [**Gemini: A Family of Highly Capable Multimodal Models**Gemini Team, Rohan Anil, Sebastian Borgeaud, et al.*2023 · 2,951 citations*](https://md.research.elamur.ai/free/en/papers/gemini-team-2023-gemini-family-highly-capable-multimodal/)
7. [**Hierarchical Text-Conditional Image Generation with CLIP Latents**Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, et al.*2022 · 2,599 citations*](https://md.research.elamur.ai/free/en/papers/ramesh-2022-hierarchical-text-conditional-image-generation/)
8. [**Scalable Diffusion Models with Transformers**William Peebles, Saining Xie*2022 · 2,595 citations*](https://md.research.elamur.ai/free/en/papers/peebles-2022-scalable-diffusion-models-transformers/)
9. [**Improved Baselines with Visual Instruction Tuning**Haotian Liu, Chunyuan Li, Yuheng Li, et al.*2023 · 2,557 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-improved-baselines-visual-instruction-tuning/)
10. [**Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision**Chao Jia, Yinfei Yang, Ye Xia, et al.*2021 · 1,528 citations*](https://md.research.elamur.ai/free/en/papers/jia-2021-scaling-up-visual-vision-language/)
11. [**CLIPScore: A Reference-free Evaluation Metric for Image Captioning**Jack Hessel, Ari Holtzman, Maxwell Forbes, et al.*2021 · 1,498 citations*](https://md.research.elamur.ai/free/en/papers/hessel-2021-clipscore-reference-free-evaluation-metric/)
12. [**BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation**Junnan Li, Dongxu Li, Caiming Xiong, et al.*2022 · 1,463 citations*](https://md.research.elamur.ai/free/en/papers/li-2022-blip-bootstrapping-language-image-pre/)
13. [**DINOv2: Learning Robust Visual Features without Supervision**Maxime Oquab, Timothee Darcet, Theo Moutakanni, et al.*2023 · 1,425 citations*](https://md.research.elamur.ai/free/en/papers/oquab-2023-dinov2-learning-robust-visual-features/)
14. [**LAION-5B: An open large-scale dataset for training next generation image-text models**Christoph Schuhmann, Romain Beaumont, Richard Vencu, et al.*2022 · 1,400 citations*](https://md.research.elamur.ai/free/en/papers/schuhmann-2022-laion-5b-open-large-scale/)
15. [**A Comprehensive Overview of Large Language Models**Humza Naveed, Asad Ullah Khan, Shi Qiu, et al.*2023 · 1,286 citations*](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-large-language-models/)
16. [**The Rise and Potential of Large Language Model Based Agents: A Survey**Zhiheng Xi, Wenxiang Chen, Xin Guo, et al.*2023 · 1,262 citations*](https://md.research.elamur.ai/free/en/papers/xi-2023-rise-potential-large-language/)
17. [**MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI**Xiang Yue, Yuansheng Ni, Kai Zhang, et al.*2023 · 948 citations*](https://md.research.elamur.ai/free/en/papers/yue-2023-mmmu-massive-multi-discipline-multimodal/)
18. [**ImageBind: One Embedding Space To Bind Them All**Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, et al.*2023 · 943 citations*](https://md.research.elamur.ai/free/en/papers/girdhar-2023-imagebind-one-embedding-space-bind/)
19. [**Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond**Jinze Bai, Shuai Bai, Shusheng Yang, et al.*2023 · 913 citations*](https://md.research.elamur.ai/free/en/papers/bai-2023-qwen-vl-versatile-vision-language/)
20. [**Segment Anything**Alexander Kirillov, Eric Mintun, Nikhila Ravi, et al.*2023 · 865 citations*](https://md.research.elamur.ai/free/en/papers/kirillov-2023-segment-anything/)
21. [**Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution**Peng Wang, Shuai Bai, Sinan Tan, et al.*2024 · 819 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-qwen2-vl-enhancing-vision/)
22. [**LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking**Yupan Huang, Tengchao Lv, Lei Cui, et al.*2022 · 774 citations*](https://md.research.elamur.ai/free/en/papers/huang-2022-layoutlmv3-pre-training-document-ai/)
23. [**ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision**Wonjae Kim, Bokyung Son, Ildoo Kim*2021 · 762 citations*](https://md.research.elamur.ai/free/en/papers/kim-2021-vilt-vision-language-transformer-convolution/)
24. [**ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning**Ahmed Masry, Do Xuan Long, Jia Qing Tan, et al.*2022 · 744 citations*](https://md.research.elamur.ai/free/en/papers/masry-2022-chartqa-benchmark-question-answering-about/)
25. [**HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face**Yongliang Shen, Kaitao Song, Xu Tan, et al.*2023 · 737 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-hugginggpt-solving-ai-tasks-chatgpt/)
26. [**CoCa: Contrastive Captioners are Image-Text Foundation Models**Jiahui Yu, Zirui Wang, Vijay Vasudevan, et al.*2022 · 646 citations*](https://md.research.elamur.ai/free/en/papers/yu-2022-coca-contrastive-captioners-image-text/)
27. [**FNet: Mixing Tokens with Fourier Transforms**James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, et al.*2022 · 593 citations*](https://md.research.elamur.ai/free/en/papers/thorp-2022-fnet-mixing-tokens-fourier-transforms/)
28. [**OCR-free Document Understanding Transformer**Geewook Kim, Teakgyu Hong, Moonbin Yim, et al.*2021 · 580 citations*](https://md.research.elamur.ai/free/en/papers/kim-2021-ocr-free-document-understanding-transformer/)
29. [**Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time**Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, et al.*2022 · 474 citations*](https://md.research.elamur.ai/free/en/papers/wortsman-2022-model-soups-averaging-weights-multiple/)
30. [**SimVLM: Simple Visual Language Model Pretraining with Weak Supervision**Zirui Wang, Jiahui Yu, Adams Wei Yu, et al.*2021 · 459 citations*](https://md.research.elamur.ai/free/en/papers/wang-2021-simvlm-simple-visual-language-model/)
31. [**OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework**Peng Wang, An Yang, Rui Men, et al.*2022 · 452 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-ofa-unifying-architectures-tasks-modalities/)
32. [**mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality**Qinghao Ye, Haiyang Xu, Guohai Xu, et al.*2023 · 410 citations*](https://md.research.elamur.ai/free/en/papers/ye-2023-mplug-owl-modularization-empowers-large/)
33. [**InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks**Zhe Chen, Jiannan Wu, Wenhai Wang, et al.*2023 · 409 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-internvl-scaling-up-vision/)
34. [**Training data-efficient image transformers & distillation through attention**Hugo Touvron, Matthieu Cord, Matthijs Douze, et al.*2020 · 405 citations*](https://md.research.elamur.ai/free/en/papers/touvron-2020-training-data-efficient-image-transformers/)
35. [**Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis**Chaoyou Fu, Yuhan Dai, Yongdong Luo, et al.*2024 · 403 citations*](https://md.research.elamur.ai/free/en/papers/fu-2024-video-mme-first-ever-comprehensive/)
36. [**CogAgent: A Visual Language Model for GUI Agents**Wenyi Hong, Weihan Wang, Qingsong Lv, et al.*2023 · 353 citations*](https://md.research.elamur.ai/free/en/papers/hong-2023-cogagent-visual-language-model/)
37. [**SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities**Dong Zhang, Shimin Li, Xin Zhang, et al.*2023 · 346 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-speechgpt-empowering-large-language-models/)
38. [**PaLI: A Jointly-Scaled Multilingual Language-Image Model**Xi Chen, Xiao Wang, Soravit Changpinyo, et al.*2022 · 339 citations*](https://md.research.elamur.ai/free/en/papers/chen-2022-pali-jointly-scaled-multilingual-language/)
39. [**MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities**Weihao Yu, Zhengyuan Yang, Linjie Li, et al.*2023 · 328 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-mm-vet-evaluating-large-multimodal/)
40. [**LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention**Renrui Zhang, Jiaming Han, Chris Liu, et al.*2023 · 322 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-llama-adapter-efficient-fine-tuning/)
41. [**TIES-Merging: Resolving Interference When Merging Models**Prateek Yadav, Derek Tam, Leshem Choshen, et al.*2023 · 306 citations*](https://md.research.elamur.ai/free/en/papers/yadav-2023-ties-merging-resolving-interference-merging/)
42. [**Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs**Shengbang Tong, Zhuang Liu, Yuexiang Zhai, et al.*2024 · 297 citations*](https://md.research.elamur.ai/free/en/papers/tong-2024-eyes-wide-shut-exploring-visual/)
43. [**Multimodal Chain-of-Thought Reasoning in Language Models**Zhuosheng Zhang, Aston Zhang, Mu Li, et al.*2023 · 296 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-multimodal-chain-thought-reasoning-language/)
44. [**Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language**Andy Zeng, Maria Attarian, Brian Ichter, et al.*2022 · 285 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2022-socratic-models-composing-zero-shot/)
45. [**Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling**Zhe Chen, Weiyun Wang, Yue Cao, et al.*2024 · 276 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-expanding-performance-boundaries-open/)
46. [**Language Models can Solve Computer Tasks**Geunwoo Kim, Pierre Baldi, Stephen McAleer*2023 · 239 citations*](https://md.research.elamur.ai/free/en/papers/kim-2023-language-models-solve-computer-tasks/)
47. [**Perceiver: General Perception with Iterative Attention**Andrew Jaegle, Felix Gimeno, Andrew Brock, et al.*2021 · 238 citations*](https://md.research.elamur.ai/free/en/papers/jaegle-2021-perceiver-general-perception-iterative-attention/)
48. [**Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey**Zeyu Han, Chao Gao, Jinyang Liu, et al.*2024 · 236 citations*](https://md.research.elamur.ai/free/en/papers/han-2024-parameter-efficient-fine-tuning-large/)
49. [**Transformers learn in-context by gradient descent**Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, et al.*2022 · 236 citations*](https://md.research.elamur.ai/free/en/papers/oswald-2022-transformers-learn-context-gradient-descent/)
50. [**DyLoRA: Parameter-Efficient Tuning of Pre-trained Models using Dynamic Search-Free Low-Rank Adaptation**Mojtaba Valipour, Mehdi Rezagholizadeh, Ivan Kobyzev, et al.*2023 · 201 citations*](https://md.research.elamur.ai/free/en/papers/valipour-2023-dylora-parameter-efficient-tuning-pre/)
51. [**Merging Models with Fisher-Weighted Averaging**Michael Matena, Colin Raffel*2021 · 190 citations*](https://md.research.elamur.ai/free/en/papers/matena-2021-merging-models-fisher-weighted-averaging/)
52. [**Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models**Matt Deitke, Christopher Clark, Sangho Lee, et al.*2024 · 184 citations*](https://md.research.elamur.ai/free/en/papers/deitke-2024-molmo-pixmo-open-weights-open/)
53. [**Eliciting Latent Predictions from Transformers with the Tuned Lens**Nora Belrose, Igor Ostrovsky, Lev McKinney, et al.*2023 · 179 citations*](https://md.research.elamur.ai/free/en/papers/belrose-2023-eliciting-latent-predictions-transformers-tuned/)
54. [**Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs**Shengbang Tong, Ellis Brown, Penghao Wu, et al.*2024 · 173 citations*](https://md.research.elamur.ai/free/en/papers/tong-2024-cambrian-1-fully-open-vision/)
55. [**Multi-Modal Sarcasm Detection via Cross-Modal Graph Convolutional Network**Bin Liang, Chenwei Lou, Xiang Li, et al.*2022 · 172 citations*](https://md.research.elamur.ai/free/en/papers/liang-2022-multi-modal-sarcasm-detection-cross/)
56. [**WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models**Hongliang He, Wenlin Yao, Kaixin Ma, et al.*2024 · 168 citations*](https://md.research.elamur.ai/free/en/papers/he-2024-webvoyager-building-end-end-web/)
57. [**Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision**Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, et al.*2023 · 163 citations*](https://md.research.elamur.ai/free/en/papers/burns-2023-weak-strong-generalization-eliciting-strong/)
58. [**Towards Multi-Modal Sarcasm Detection via Hierarchical Congruity Modeling with Knowledge Enhancement**Hui Liu, Wenya Wang, Haoliang Li*2022 · 132 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-multi-modal-sarcasm-detection-hierarchical/)
59. [**Analyzing Transformers in Embedding Space**Guy Dar, Mor Geva, Ankit Gupta, et al.*2023 · 129 citations*](https://md.research.elamur.ai/free/en/papers/dar-2023-analyzing-transformers-embedding-space/)
60. [**Sequence Parallelism: Long Sequence Training from System Perspective**Shenggui Li, Fuzhao Xue, Chaitanya Baranwal, et al.*2023 · 128 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-sequence-parallelism-long-sequence-training/)
61. [**SemDeDup: Data-efficient learning at web-scale through semantic deduplication**Amro Abbas, Kushal Tirumala, Dániel Simig, et al.*2023 · 94 citations*](https://md.research.elamur.ai/free/en/papers/abbas-2023-semdedup-data-efficient-learning-web/)
62. [**Dynamic Routing Transformer Network for Multimodal Sarcasm Detection**Yuan Tian, Nan Xu, Ruike Zhang, et al.*2023 · 66 citations*](https://md.research.elamur.ai/free/en/papers/tian-2023-dynamic-routing-transformer-network-multimodal/)
63. [**Multi-modal Stance Detection: New Datasets and Model**Bin Liang, Ang Li, Jingqian Zhao, et al.*2024 · 28 citations*](https://md.research.elamur.ai/free/en/papers/liang-2024-multi-modal-stance-detection-new/)
64. [**Dating ancient manuscripts using radiocarbon and AI-based writing style analysis**Mladen Popovic, Maruf A. Dhali, Lambert Schomaker, et al.*2024 · 23 citations*](https://md.research.elamur.ai/free/en/papers/popovic-2024-dating-ancient-manuscripts-using-radiocarbon/)
65. [**CofiPara: A Coarse-to-fine Paradigm for Multimodal Sarcasm Target Identification with Large Multimodal Models**Zixin Chen, Hongzhan Lin, Ziyang Luo, et al.*2024 · 20 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-cofipara-coarse-fine-paradigm-multimodal/)
66. [**2*n is better than n²: Decomposing Event Coreference Resolution into Two Tractable Problems**Shafiuddin Rehan Ahmed, Abhijnan Nath, James H. Martin, et al.*2023 · 17 citations*](https://md.research.elamur.ai/free/en/papers/ahmed-2023-2-n-is-better-than/)
67. [**AraSpell: A Deep Learning Approach for Arabic Spelling Correction**Mahmoud Salhab, Faisal Abu-Khzam*2024 · 15 citations*](https://md.research.elamur.ai/free/en/papers/salhab-2024-araspell-deep-learning-approach-arabic/)
68. [**Multimodal Cross-Document Event Coreference Resolution Using Linear Semantic Transfer and Mixed-Modality Ensembles**Abhijnan Nath, Huma Jamil, Shafiuddin Rehan Ahmed, et al.*2024 · 7 citations*](https://md.research.elamur.ai/free/en/papers/nath-2024-multimodal-cross-document-event-coreference/)
69. [**Acquiring Linguistic Knowledge from Multimodal Input**Theodor Amariucai, Alexander Scott Warstadt*2023 · 6 citations*](https://md.research.elamur.ai/free/en/papers/amariucai-2023-acquiring-linguistic-knowledge-multimodal-input/)
70. [**HATFormer: Historic Handwritten Arabic Text Recognition with Transformers**Adrian Chan, Anupam Mijar, Mehreen Saeed, et al.*2024 · 5 citations*](https://md.research.elamur.ai/free/en/papers/chan-2024-hatformer-historic-handwritten-arabic-text/)
71. [**Hebrew Diacritics Restoration using Visual Representation**Yair Elboher, Yuval Pinter*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/elboher-2026-hebrew-diacritics-restoration-visual-representation/)
72. [**Dreaming Out Loud: A Self-Synthesis Approach For Training Vision-Language Models With Developmentally Plausible Data**Badr AlKhamissi, Yingtian Tang, Abdülkadir Gökce, et al.*2024 · 1 citation*](https://md.research.elamur.ai/free/en/papers/alkhamissi-2024-dreaming-out-loud-self-synthesis/)
73. [**Evaluating Discriminability of Vision-Language Models**Masayasu Muraoka, Naoaki Okazaki*2026*](https://md.research.elamur.ai/free/en/papers/muraoka-2026-evaluating-discriminability-vision-language-models/)
74. [**MUSIA: Multilingual Story Illustration Corpus for Cross-Cultural Alignment and Generation**Krishna Tewari, Supriya Chanda, Nirmit Patil, et al.*2026*](https://md.research.elamur.ai/free/en/papers/tewari-2026-musia-multilingual-story-illustration-corpus/)
75. [**Nepal Script Text Recognition from Ancient Artifacts: Challenges and Opportunities**Swornim Nakarmi, Sarin Sthapit, Sahil Ratna Tuladhar, et al.*2026*](https://md.research.elamur.ai/free/en/papers/nakarmi-2026-nepal-script-text-recognition-ancient/)
76. [**PARL: Prompt-based Agents for Reinforcement Learning**Yarik Menchaca Resendiz, Roman Klinger*2026*](https://md.research.elamur.ai/free/en/papers/resendiz-2026-parl-prompt-based-agents-reinforcement/)
77. [**SPARTA: Evaluating Reasoning Segmentation Robustness through Black-Box Adversarial Paraphrasing in Text Autoencoder Latent Space**Viktoriia Zinkovich, Anton Antonov, Andrei Spiridonov, et al.*2026*](https://md.research.elamur.ai/free/en/papers/zinkovich-2026-sparta-evaluating-reasoning-segmentation-robustness/)
78. [**Survey on Vision-language Pre-training (视觉语言预训练综述)**Jiong Yin (殷炯), Zhedong Zhang (张哲东), Yuhan Gao (高宇涵), et al.*2022*](https://md.research.elamur.ai/free/en/papers/yin-2022-vision-language-pre/)

1,869 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/dosovitskiy-2020-image-worth-16x16-words-transformers/) — An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale