# ADVISE: Symbolism and External Knowledge for Decoding Advertisements

arXiv: https://arxiv.org/abs/1711.06666 · HTML: https://mlsanity.com/paper/1711.06666/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2017.

## Built on by (most cited first, 21 of 21)

- [MetaCLUE: Towards Comprehensive Visual Metaphors Research](https://mlsanity.com/paper/2212.09898/) (2022)
- [Text-to-Image Generation for Abstract Concepts](https://mlsanity.com/paper/2309.14623/) (2023)
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- [Analyzing Online Political Advertisements](https://mlsanity.com/paper/2105.04047/) (2021)
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- [Benchmarking VLMs' Reasoning About Persuasive Atypical Images](https://mlsanity.com/paper/2409.10719/) (2024)
- [TSI: an Ad Text Strength Indicator using Text-to-CTR and Semantic-Ad-Similarity](https://mlsanity.com/paper/2108.08226/) (2021)
- [Seeing the Intangible: Survey of Image Classification into High-Level and Abstract Categories](https://mlsanity.com/paper/2308.10562/) (2023)
- [Symbolic image detection using scene and knowledge graphs](https://mlsanity.com/paper/2206.04863/) (2022)
- [Looking Beyond the Obvious: A Survey on Abstract Concept Recognition for Video Understanding](https://mlsanity.com/paper/2508.20765/) (2025)
- [Don't Buy it! Reassessing the Ad Understanding Abilities of Contrastive Multimodal Models](https://mlsanity.com/paper/2405.20846/) (2024)
- [Stitching Gaps: Fusing Situated Perceptual Knowledge with Vision Transformers for High-Level Image Classification](https://mlsanity.com/paper/2402.19339/) (2024)
- [For a semiotic AI: Bridging computer vision and visual semiotics for computational observation of large scale facial image archives](https://mlsanity.com/paper/2407.03268/) (2024)
- [SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysis](https://mlsanity.com/paper/2108.02572/) (2021)
- [VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Search](https://mlsanity.com/paper/2108.02725/) (2021)
- [Text is NOT Enough: Integrating Visual Impressions into Open-domain Dialogue Generation](https://mlsanity.com/paper/2109.05778/) (2021)
- [Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors](https://mlsanity.com/paper/2202.02830/) (2022)
- [Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks](https://mlsanity.com/paper/2203.11011/) (2022)
- [Explanation-Guided Fairness Testing through Genetic Algorithm](https://mlsanity.com/paper/2205.08335/) (2022)
- [Using Graph Neural Networks for Program Termination](https://mlsanity.com/paper/2207.14648/) (2022)
- [Tribe or Not? Critical Inspection of Group Differences Using TribalGram](https://mlsanity.com/paper/2303.09664/) (2023)

## Abstract (arXiv)

In order to convey the most content in their limited space, advertisements embed references to outside knowledge via symbolism. For example, a motorcycle stands for adventure (a positive property the ad wants associated with the product being sold), and a gun stands for danger (a negative property to dissuade viewers from undesirable behaviors). We show how to use symbolic references to better understand the meaning of an ad. We further show how anchoring ad understanding in general-purpose object recognition and image captioning improves results. We formulate the ad understanding task as matching the ad image to human-generated statements that describe the action that the ad prompts, and the rationale it provides for taking this action. Our proposed method outperforms the state of the art on this task, and on an alternative formulation of question-answering on ads. We show additional applications of our learned representations for matching ads to slogans, and clustering ads according to their topic, without extra training.