Do I look like a `cat.n.01` to you? A Taxonomy Image Generation Benchmark
This work addresses the automation of structured data curation for taxonomy enrichment, representing an incremental advancement by applying existing methods to a new domain.
The paper tackles the problem of generating images for taxonomy concepts using text-to-image models in a zero-shot setup, proposing a benchmark that shows Playground-v2 and FLUX outperform others, with rankings differing from standard tasks.
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.