# Image Captioning

arXiv: https://arxiv.org/abs/1805.09137 · HTML: https://mlsanity.com/paper/1805.09137/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2018.

## Built on by (most cited first, 14 of 14)

- [mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video](https://mlsanity.com/paper/2302.00402/) (2023)
- [A-JEPA: Joint-Embedding Predictive Architecture Can Listen](https://mlsanity.com/paper/2311.15830/) (2023)
- [Vision-Language Modeling Meets Remote Sensing: Models, Datasets and Perspectives](https://mlsanity.com/paper/2505.14361/) (2025)
- [Perspectives and Prospects on Transformer Architecture for Cross-Modal Tasks with Language and Vision](https://mlsanity.com/paper/2103.04037/) (2021)
- [Beyond Generation: Harnessing Text to Image Models for Object Detection and Segmentation](https://mlsanity.com/paper/2309.05956/) (2023)
- [A TextGCN-Based Decoding Approach for Improving Remote Sensing Image Captioning](https://mlsanity.com/paper/2409.18467/) (2024)
- [MLink: Linking Black-Box Models from Multiple Domains for Collaborative Inference](https://mlsanity.com/paper/2209.13883/) (2022)
- [MIVC: Multiple Instance Visual Component for Visual-Language Models](https://mlsanity.com/paper/2312.17109/) (2023)
- [FaceGemma: Enhancing Image Captioning with Facial Attributes for Portrait Images](https://mlsanity.com/paper/2309.13601/) (2023)
- [Descripci'on autom'atica de secciones delgadas de rocas: una aplicaci'on Web](https://mlsanity.com/paper/2402.15039/) (2024)
- [Feedback-based Modal Mutual Search for Attacking Vision-Language Pre-training Models](https://mlsanity.com/paper/2409.06726/) (2024)
- [Reasoning-Aware Query-Focused Summarization over Multi-Table Data](https://mlsanity.com/paper/2412.08970/) (2024)
- [MELLA: Bridging Linguistic Capability and Cultural Groundedness for Low-Resource Language MLLMs](https://mlsanity.com/paper/2508.05502/) (2025)
- [The Language of Touch: Translating Vibrations into Text with Dual-Branch Learning](https://mlsanity.com/paper/2603.26804/) (2026)

## Abstract (arXiv)

This paper discusses and demonstrates the outcomes from our experimentation on Image Captioning. Image captioning is a much more involved task than image recognition or classification, because of the additional challenge of recognizing the interdependence between the objects/concepts in the image and the creation of a succinct sentential narration. Experiments on several labeled datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. As a toy application, we apply image captioning to create video captions, and we advance a few hypotheses on the challenges we encountered.