# Journalistic Guidelines Aware News Image Captioning

arXiv: https://arxiv.org/abs/2109.02865 · HTML: https://mlsanity.com/paper/2109.02865/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 47 references that are in the map.
- First posted on arXiv 7 Sep 2021.

## Builds on (most cited first, 30 of 32)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Microsoft COCO Captions: Data Collection and Evaluation Server](https://mlsanity.com/paper/1504.00325/) (2015)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [Deep Visual-Semantic Alignments for Generating Image Descriptions](https://mlsanity.com/paper/1412.2306/) (2014)
- [Self-critical Sequence Training for Image Captioning](https://mlsanity.com/paper/1612.00563/) (2016)
- [AllenNLP: A Deep Semantic Natural Language Processing Platform](https://mlsanity.com/paper/1803.07640/) (2018)
- [Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning](https://mlsanity.com/paper/1612.01887/) (2016)
- [DenseCap: Fully Convolutional Localization Networks for Dense Captioning](https://mlsanity.com/paper/1511.07571/) (2015)
- [Neural Baby Talk](https://mlsanity.com/paper/1803.09845/) (2018)
- [Good News, Everyone! Context driven entity-aware captioning for news images](https://mlsanity.com/paper/1904.01475/) (2019)
- [Probing the Need for Visual Context in Multimodal Machine Translation](https://mlsanity.com/paper/1903.08678/) (2019)
- [Convolutional Image Captioning](https://mlsanity.com/paper/1711.09151/) (2017)
- [Transform and Tell: Entity-Aware News Image Captioning](https://mlsanity.com/paper/2004.08070/) (2020)
- [Fashion Captioning: Towards Generating Accurate Descriptions with Semantic Rewards](https://mlsanity.com/paper/2008.02693/) (2020)
- [Image Captioning through Image Transformer](https://mlsanity.com/paper/2004.14231/) (2020)
- [Captioning Images with Diverse Objects](https://mlsanity.com/paper/1606.07770/) (2016)
- [Entity-aware Image Caption Generation](https://mlsanity.com/paper/1804.07889/) (2018)
- [Towards Unique and Informative Captioning of Images](https://mlsanity.com/paper/2009.03949/) (2020)
- [BreakingNews: Article Annotation by Image and Text Processing](https://mlsanity.com/paper/1603.07141/) (2016)
- [Show, Edit and Tell: A Framework for Editing Image Captions](https://mlsanity.com/paper/2003.03107/) (2020)

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

- [Visually-Aware Context Modeling for News Image Captioning](https://mlsanity.com/paper/2308.08325/) (2023)
- [Show, Interpret and Tell: Entity-aware Contextualised Image Captioning in Wikipedia](https://mlsanity.com/paper/2209.10474/) (2022)
- [Focus! Relevant and Sufficient Context Selection for News Image Captioning](https://mlsanity.com/paper/2212.00843/) (2022)
- ["Let's not Quote out of Context": Unified Vision-Language Pretraining for Context Assisted Image Captioning](https://mlsanity.com/paper/2306.00931/) (2023)
- [AICAttack: Adversarial Image Captioning Attack with Attention-Based Optimization](https://mlsanity.com/paper/2402.11940/) (2024)
- [Controllable Contextualized Image Captioning: Directing the Visual Narrative through User-Defined Highlights](https://mlsanity.com/paper/2407.11449/) (2024)
- [Knowledge Completes the Vision: A Multimodal Entity-aware Retrieval-Augmented Generation Framework for News Image Captioning](https://mlsanity.com/paper/2511.21002/) (2025)
- [EAMA : Entity-Aware Multimodal Alignment Based Approach for News Image Captioning](https://mlsanity.com/paper/2402.19404/) (2024)
- [Rule-driven News Captioning](https://mlsanity.com/paper/2403.05101/) (2024)
- [Surveying the Landscape of Image Captioning Evaluation: A Comprehensive Taxonomy, Trends and Metrics Analysis](https://mlsanity.com/paper/2408.04909/) (2024)
- [Controllable Image Captioning](https://mlsanity.com/paper/2204.13324/) (2022)
- [Tackling Multiple Tasks with One Single Learning Framework](https://mlsanity.com/paper/2206.06322/) (2022)
- [A Masked Bounding-Box Selection Based ResNet Predictor for Text Rotation Prediction](https://mlsanity.com/paper/2209.09198/) (2022)
- [How to Understand Named Entities: Using Common Sense for News Captioning](https://mlsanity.com/paper/2403.06520/) (2024)

## Abstract (arXiv)

The task of news article image captioning aims to generate descriptive and informative captions for news article images. Unlike conventional image captions that simply describe the content of the image in general terms, news image captions follow journalistic guidelines and rely heavily on named entities to describe the image content, often drawing context from the whole article they are associated with. In this work, we propose a new approach to this task, motivated by caption guidelines that journalists follow. Our approach, Journalistic Guidelines Aware News Image Captioning (JoGANIC), leverages the structure of captions to improve the generation quality and guide our representation design. Experimental results, including detailed ablation studies, on two large-scale publicly available datasets show that JoGANIC substantially outperforms state-of-the-art methods both on caption generation and named entity related metrics.