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Current image-text evaluation often relies on a global score, which gives limited insight into which semantic details are supported by the image. Explainable metrics provide rationales, but the visual evidence supporting those rationales can still be difficult to verify. We introduce VisExMEM, a reference-free evaluato...
The results show that annotation reporting in NLP has improved over time but remains uneven, and they establish a scalable framework and bare-minimum reporting recommendations for making human annotation more reliable, reproducible, and interpretable.
AVShift is introduced, the first German benchmark for systematically evaluating AV under multiple distribution shifts, and feature analysis reveals stylistic features that remain stable across genres, while their relative importance varies depending on the specific genre transition.
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