mlsanityData to 14 Oct 2026

ADVISE: Symbolism and External Knowledge for Decoding Advertisements

arXiv 1711.06666 ↗First posted 17 Nov 2017cs.CVcited by 21

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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.

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

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Citing papers per year: 2021: 6; 2022: 6; 2023: 4; 2024: 4; 2025: 1.

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  1. 2022MetaCLUE: Towards Comprehensive Visual Metaphors Researchcited by 28
  2. 2023Text-to-Image Generation for Abstract Conceptscited by 18
  3. 2021Affect2MM: Affective Analysis of Multimedia Content Using Emotion Causalitycited by 17
  4. 2021Analyzing Online Political Advertisementscited by 11
  5. 2023A Video Is Worth 4096 Tokens: Verbalize Videos To Understand Them In Zero Shotcited by 11
  6. 2024Benchmarking VLMs' Reasoning About Persuasive Atypical Imagescited by 5
  7. 2021TSI: an Ad Text Strength Indicator using Text-to-CTR and Semantic-Ad-Similaritycited by 4
  8. 2023Seeing the Intangible: Survey of Image Classification into High-Level and Abstract Categoriescited by 4
  9. 2022Symbolic image detection using scene and knowledge graphscited by 3
  10. 2025Looking Beyond the Obvious: A Survey on Abstract Concept Recognition for Video Understandingcited by 3
  11. 2024Don't Buy it! Reassessing the Ad Understanding Abilities of Contrastive Multimodal Modelscited by 2
  12. 2024Stitching Gaps: Fusing Situated Perceptual Knowledge with Vision Transformers for High-Level Image Classificationcited by 1
9 more
  1. 2024For a semiotic AI: Bridging computer vision and visual semiotics for computational observation of large scale facial image archivescited by 1
  2. 2021SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysiscited by 0
  3. 2021VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Searchcited by 0
  4. 2021Text is NOT Enough: Integrating Visual Impressions into Open-domain Dialogue Generationcited by 0
  5. 2022Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectorscited by 0
  6. 2022Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networkscited by 0
  7. 2022Explanation-Guided Fairness Testing through Genetic Algorithmcited by 0
  8. 2022Using Graph Neural Networks for Program Terminationcited by 0
  9. 2023Tribe or Not? Critical Inspection of Group Differences Using TribalGramcited by 0