Social Media Ready Caption Generation for Brands
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Abstract (arXiv)
Social media advertisements are key for brand marketing, aiming to attract consumers with captivating captions and pictures or logos. While previous research has focused on generating captions for general images, incorporating brand personalities into social media captioning remains unexplored. Brand personalities are shown to be affecting consumers' behaviours and social interactions and thus are proven to be a key aspect of marketing strategies. Current open-source multimodal LLMs are not directly suited for this task. Hence, we propose a pipeline solution to assist brands in creating engaging social media captions that align with the image and the brand personalities. Our architecture is based on two parts: a the first part contains an image captioning model that takes in an image that the brand wants to post online and gives a plain English caption; b the second part takes in the generated caption along with the target brand personality and outputs a catchy personality-aligned social media caption. Along with brand personality, our system also gives users the flexibility to provide hashtags, Instagram handles, URLs, and named entities they want the caption to contain, making the captions more semantically related to the social media handles. Comparative evaluations against various baselines demonstrate the effectiveness of our approach, both qualitatively and quantitatively.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 31 references that are in the map. First posted on arXiv 3 Jan 2024.
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16 of 31 references.
References · 16
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- 2014Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)cited by 94
- 2019A Simple Baseline for Audio-Visual Scene-Aware Dialogcited by 52
- 2019Factor Graph Attentioncited by 41
4 more
- 2017Attend to You: Personalized Image Captioning with Context Sequence Memory Networkscited by 35
- 2017High-Order Attention Models for Visual Question Answeringcited by 22
- 2014Fisher Vectors Derived from Hybrid Gaussian-Laplacian Mixture Models for Image Annotationcited by 16
- 2020An Integrated Approach for Improving Brand Consistency of Web Content: Modeling, Analysis and Recommendationcited by 2
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- 2025From Pixels to Posts: Retrieval-Augmented Fashion Captioning and Hashtag Generation
- 2024XMeCap: Meme Caption Generation with Sub-Image Adaptability
- 2025CaptionQA: Is Your Caption as Useful as the Image Itself?
- 2024Revisit Large-Scale Image-Caption Data in Pre-training Multimodal Foundation Models
- 2024Improving face generation quality and prompt following with synthetic captions
- 2023"Let's not Quote out of Context": Unified Vision-Language Pretraining for Context Assisted Image Captioning
- 2025Can LLMs Simulate Social Media Engagement? A Study on Action-Guided Response Generation
- 2023Image Captioning with Multi-Context Synthetic Data