mlsanityData to 7 Oct 2026

A Comprehensive Survey of Deep Learning for Image Captioning

arXiv 1810.04020 ↗First posted 6 Oct 2018cs.CV cs.LG stat.MLcited by 213

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Cited by 38 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Generating a description of an image is called image captioning. Image captioning requires to recognize the important objects, their attributes and their relationships in an image. It also needs to generate syntactically and semantically correct sentences. Deep learning-based techniques are capable of handling the complexities and challenges of image captioning. In this survey paper, we aim to present a comprehensive review of existing deep learning-based image captioning techniques. We discuss the foundation of the techniques to analyze their performances, strengths and limitations. We also discuss the datasets and the evaluation metrics popularly used in deep learning based automatic image captioning.

Key facts. Cited by 213 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Oct 2018.

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Citing papers per year: 2021: 30; 2022: 40; 2023: 50; 2024: 42; 2025: 35; 2026: 16.

213 papers.

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  1. 2021Multimodal Few-Shot Learning with Frozen Language Modelscited by 657
  2. 2024Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Surveycited by 641
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  9. 2021From Show to Tell: A Survey on Deep Learning-based Image Captioningcited by 137
  10. 2023Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report Generationcited by 114
  11. 2023Foundational Models Defining a New Era in Vision: A Survey and Outlookcited by 109
  12. 2023Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlookcited by 109
38 more
  1. 2021Understanding and Evaluating Racial Biases in Image Captioningcited by 108
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  3. 2024VL-Trojan: Multimodal Instruction Backdoor Attacks against Autoregressive Visual Language Modelscited by 83
  4. 2024Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Surveycited by 74
  5. 2023A Comprehensive Survey on Applications of Transformers for Deep Learning Taskscited by 70
  6. 2024TabPedia: Towards Comprehensive Visual Table Understanding with Concept Synergycited by 70
  7. 2022Physical Adversarial Attack meets Computer Vision: A Decade Surveycited by 66
  8. 2024AesBench: An Expert Benchmark for Multimodal Large Language Models on Image Aesthetics Perceptioncited by 63
  9. 2023XrayGPT: Chest Radiographs Summarization using Medical Vision-Language Modelscited by 61
  10. 2023One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Eracited by 55
  11. 2021Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advancescited by 54
  12. 2024AesExpert: Towards Multi-modality Foundation Model for Image Aesthetics Perceptioncited by 53
  13. 2021Adversarial Text-to-Image Synthesis: A Reviewcited by 52
  14. 2023Jack of All Tasks, Master of Many: Designing General-purpose Coarse-to-Fine Vision-Language Modelcited by 52
  15. 2022Deep Learning Approaches on Image Captioning: A Reviewcited by 43
  16. 2024Immune: Improving Safety Against Jailbreaks in Multi-modal LLMs via Inference-Time Alignmentcited by 42
  17. 2022Gender Artifacts in Visual Datasetscited by 38
  18. 2022Visual Attention Methods in Deep Learning: An In-Depth Surveycited by 37
  19. 2023OT-Attack: Enhancing Adversarial Transferability of Vision-Language Models via Optimal Transport Optimizationcited by 36
  20. 2022A Review of Emerging Research Directions in Abstract Visual Reasoningcited by 33
  21. 2021INVIGORATE: Interactive Visual Grounding and Grasping in Cluttercited by 32
  22. 2024Graph Transformers: A Surveycited by 32
  23. 2022M^4I: Multi-modal Models Membership Inferencecited by 30
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  25. 2021Artificial Intelligence in Drug Discovery: Applications and Techniquescited by 28
  26. 2023Retrieval-augmented Image Captioningcited by 28
  27. 2021Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AIcited by 26
  28. 2021Reasoning over Vision and Language: Exploring the Benefits of Supplemental Knowledgecited by 24
  29. 2021Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Surveycited by 24
  30. 2023From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunitiescited by 24
  31. 2023Mathematical Language Models: A Surveycited by 24
  32. 2022Prompt-based Learning for Unpaired Image Captioningcited by 23
  33. 2023IMITATE: Clinical Prior Guided Hierarchical Vision-Language Pre-trainingcited by 23
  34. 2025Octopus: Alleviating Hallucination via Dynamic Contrastive Decodingcited by 23
  35. 2022Vision Transformers in Medical Computer Vision -- A Contemplative Retrospectioncited by 22
  36. 2021Levels of explainable artificial intelligence for human-aligned conversational explanationscited by 21
  37. 2023Zero-shot Referring Expression Comprehension via Structural Similarity Between Images and Captionscited by 21
  38. 2022Controllable Data Generation by Deep Learning: A Reviewcited by 20

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