Surprise-Guided MergeSort uses a VLM-based composite surprise scorer to prioritize human comparisons inside a MergeSort scheduler, skipping up to 535 pairs per session and raising Kendall's τ by 6-12 points over Active Elo at fixed budget across six benchmarks.
arXiv preprint arXiv:2202.04823 (2022)
2 Pith papers cite this work. Polarity classification is still indexing.
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MetaRanker uses active learning with human preference judgments and lightweight VLM priors to rank metalens images by semantic interpretability, achieving closer human alignment with roughly 80% fewer pairwise annotations than exhaustive comparison.
citing papers explorer
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Surprise-Guided MergeSort: Budget-Efficient Human-in-the-Loop Ranking via Adaptive Comparison Scheduling
Surprise-Guided MergeSort uses a VLM-based composite surprise scorer to prioritize human comparisons inside a MergeSort scheduler, skipping up to 535 pairs per session and raising Kendall's τ by 6-12 points over Active Elo at fixed budget across six benchmarks.
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MetaRanker: Human-in-the-loop Active Ranking for Metalens Image Quality
MetaRanker uses active learning with human preference judgments and lightweight VLM priors to rank metalens images by semantic interpretability, achieving closer human alignment with roughly 80% fewer pairwise annotations than exhaustive comparison.