Do I look like a `cat.n.01` to you? A Taxonomy Image Generation
  Benchmark
This paper explores the feasibility of using text-to-image models in a
zero-shot setup to generate images for taxonomy concepts. While text-based
methods for taxonomy enrichment are well-established, the potential of the
visual dimension remains unexplored. To address this, we propose a
comprehensive benchmark for Taxonomy Image Generation that assesses models'
abilities to understand taxonomy concepts and generate relevant, high-quality
images. The benchmark includes common-sense and randomly sampled WordNet
concepts, alongside the LLM generated predictions. The 12 models are evaluated
using 9 novel taxonomy-related text-to-image metrics and human feedback.
Moreover, we pioneer the use of pairwise evaluation with GPT-4 feedback for
image generation. Experimental results show that the ranking of models differs
significantly from standard T2I tasks. Playground-v2 and FLUX consistently
outperform across metrics and subsets and the retrieval-based approach performs
poorly. These findings highlight the potential for automating the curation of
structured data resources.
67d3da3ce0b767b3d0fae2a4
Viktor Moskvoretskii
extracted_confirmed
2025-03-14T07:38:54.624Z
63bbfd74141c7d395c471768
https://cdn-avatars.huggingface.co/v1/production/uploads/1673264437106-noauth.jpeg
Viktor Moskvoretskii
user
VityaVitalich
67d3da3ce0b767b3d0fae2a5
Alina Lobanova
67d3da3ce0b767b3d0fae2a6
Ekaterina Neminova
67d3da3ce0b767b3d0fae2a7
Chris Biemann
67d3da3ce0b767b3d0fae2a8
Alexander Panchenko
admin_assigned
2025-03-14T10:57:38.630Z
605473729d7c1d4d81b7e52b
https://cdn-avatars.huggingface.co/v1/production/uploads/1662046050710-605473729d7c1d4d81b7e52b.jpeg
Alexander Panchenko
user
apanc
67d3da3ce0b767b3d0fae2a9
Irina Nikishina
admin_assigned
2025-03-14T10:57:44.989Z
6458a9ed6fa580137af4c710
/avatars/8b9f99537adf30fee6aa7ae61ab7235b.svg
Irina Nikishina
user
lilaspourpre
Do I look like a `cat.n.01` to you? A Taxonomy Image Generation
  Benchmark
This paper explores the feasibility of using text-to-image models in a
zero-shot setup to generate images for taxonomy concepts. While text-based
methods for taxonomy enrichment are well-established, the potential of the
visual dimension remains unexplored. To address this, we propose a
comprehensive benchmark for Taxonomy Image Generation that assesses models'
abilities to understand taxonomy concepts and generate relevant, high-quality
images. The benchmark includes common-sense and randomly sampled WordNet
concepts, alongside the LLM generated predictions. The 12 models are evaluated
using 9 novel taxonomy-related text-to-image metrics and human feedback.
Moreover, we pioneer the use of pairwise evaluation with GPT-4 feedback for
image generation. Experimental results show that the ranking of models differs
significantly from standard T2I tasks. Playground-v2 and FLUX consistently
outperform across metrics and subsets and the retrieval-based approach performs
poorly. These findings highlight the potential for automating the curation of
structured data resources.
A benchmark evaluates text-to-image models on taxonomy image generation, demonstrating differences in model performance and highlighting the potential for automation in data curation.
text-to-image models zero-shot setup taxonomy concepts WordNet taxonomy-related text-to-image metrics pairwise evaluation GPT-4 retrieval-based approach