RePOPE: Impact of Annotation Errors on the POPE Benchmark

Yannic Neuhaus, Matthias Hein

RePOPE: Impact of Annotation Errors on the POPE Benchmark: 8 upvotes on Hugging Face Daily Papers, #13 of 17 papers on 2025-04-24. Day-by-day upvote history.

Since data annotation is costly, benchmark datasets often incorporate labels from established image datasets. In this work, we assess the impact of label errors in MSCOCO on the frequently used object hallucination benchmark POPE. We re-annotate the benchmark images and identify an imbalance in annotation errors across different subsets. Evaluating multiple models on the revised labels, which we denote as RePOPE, we observe notable shifts in model rankings, highlighting the impact of label quality. Code and data are available at https://github.com/YanNeu/RePOPE .

Paper page on Hugging Face · arXiv

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