# Image Captioning at Will: A Versatile Scheme for Effectively Injecting Sentiments into Image Descriptions

arXiv: https://arxiv.org/abs/1801.10121 · HTML: https://mlsanity.com/paper/1801.10121/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2018.

## Built on by (most cited first, 12 of 12)

- [ArtEmis: Affective Language for Visual Art](https://mlsanity.com/paper/2101.07396/) (2021)
- [I Can't Believe There's No Images! Learning Visual Tasks Using only Language Supervision](https://mlsanity.com/paper/2211.09778/) (2022)
- [Controllable Video Captioning with an Exemplar Sentence](https://mlsanity.com/paper/2112.01073/) (2021)
- [Similar Scenes arouse Similar Emotions: Parallel Data Augmentation for Stylized Image Captioning](https://mlsanity.com/paper/2108.11912/) (2021)
- [Visual Captioning at Will: Describing Images and Videos Guided by a Few Stylized Sentences](https://mlsanity.com/paper/2307.16399/) (2023)
- [Diverse Image Captioning with Grounded Style](https://mlsanity.com/paper/2205.01813/) (2022)
- [Syntax Customized Video Captioning by Imitating Exemplar Sentences](https://mlsanity.com/paper/2112.01062/) (2021)
- [Goal-driven text descriptions for images](https://mlsanity.com/paper/2108.12575/) (2021)
- [How to Describe Images in a More Funny Way? Towards a Modular Approach to Cross-Modal Sarcasm Generation](https://mlsanity.com/paper/2211.10992/) (2022)
- [See Your Heart: Psychological states Interpretation through Visual Creations](https://mlsanity.com/paper/2302.10276/) (2023)
- [ADS-Cap: A Framework for Accurate and Diverse Stylized Captioning with Unpaired Stylistic Corpora](https://mlsanity.com/paper/2308.01143/) (2023)
- [Stock Price Prediction Under Anomalous Circumstances](https://mlsanity.com/paper/2109.15059/) (2021)

## Abstract (arXiv)

Automatic image captioning has recently approached human-level performance due to the latest advances in computer vision and natural language understanding. However, most of the current models can only generate plain factual descriptions about the content of a given image. However, for human beings, image caption writing is quite flexible and diverse, where additional language dimensions, such as emotion, humor and language styles, are often incorporated to produce diverse, emotional, or appealing captions. In particular, we are interested in generating sentiment-conveying image descriptions, which has received little attention. The main challenge is how to effectively inject sentiments into the generated captions without altering the semantic matching between the visual content and the generated descriptions. In this work, we propose two different models, which employ different schemes for injecting sentiments into image captions. Compared with the few existing approaches, the proposed models are much simpler and yet more effective. The experimental results show that our model outperform the state-of-the-art models in generating sentimental (i.e., sentiment-bearing) image captions. In addition, we can also easily manipulate the model by assigning different sentiments to the testing image to generate captions with the corresponding sentiments.