mlsanityData to 13 Oct 2026

Predicting Winning Captions for Weekly New Yorker Comics

arXiv 2407.18949 ↗First posted 12 Jul 2024cs.CV cs.AIcited by 1

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Abstract (arXiv)

Image captioning using Vision Transformers (ViTs) represents a pivotal convergence of computer vision and natural language processing, offering the potential to enhance user experiences, improve accessibility, and provide textual representations of visual data. This paper explores the application of image captioning techniques to New Yorker cartoons, aiming to generate captions that emulate the wit and humor of winning entries in the New Yorker Cartoon Caption Contest. This task necessitates sophisticated visual and linguistic processing, along with an understanding of cultural nuances and humor. We propose several new baselines for using vision transformer encoder-decoder models to generate captions for the New Yorker cartoon caption contest.

Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 15 of its 26 references that are in the map. First posted on arXiv 12 Jul 2024.

Builds on

15 of 26 references.

References · 15

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  12. 2015Humor in Collective Discourse: Unsupervised Funniness Detection in the New Yorker Cartoon Caption Contestcited by 16
3 more
  1. 2015We Are Humor Beings: Understanding and Predicting Visual Humorcited by 15
  2. 2023What a Whole Slide Image Can Tell? Subtype-guided Masked Transformer for Pathological Image Captioningcited by 8
  3. 2023Enhancing image captioning with depth information using a Transformer-based frameworkcited by 3

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Citing papers per year: 2024: 1.

1 paper.

  1. 2024Have Large Vision-Language Models Mastered Art History?cited by 9