# Retracted: Deep Learning Approaches for Image Captioning: Opportunities, Challenges and Future Potential

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/retracted-deep-learning-approaches-for-image-captioning-opportunities-challenges/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Azhar Jamil,Saif Ur Rehman,Khalid Mahmood,Mónica Gracia Villar,Thomas André Prola,Isabel de la Torre Díez,Md Abdus Samad,Imran Ashraf |
| Citations | 15 |
| DOI | 10.1109/access.2024.3365528 |
| Fields | Computer Science |
| Open Access | true |
| OA Status | gold |
| OA URL | https://ieeexplore.ieee.org/ielx7/6287639/6514899/10433498.pdf |
| OpenAlex ID | https://openalex.org/W4391791507 |
| Type | article |
| Year | 2024 |

## Paper authors

- [Thomas André Prola](https://scholariq.org/researchers/thomas-andre-prola/)

## Paper journal

- [IEEE Access](https://scholariq.org/journals/ieee-access/)

## Paper primary topic

- [Multimodal Machine Learning Applications](https://scholariq.org/topics/multimodal-machine-learning-applications/)

## Paper topics

- [Multimodal Machine Learning Applications](https://scholariq.org/topics/multimodal-machine-learning-applications/)
- [Video Analysis and Summarization](https://scholariq.org/topics/video-analysis-and-summarization/)
- [Human Pose and Action Recognition](https://scholariq.org/topics/human-pose-and-action-recognition/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.