MetaEvaluator meta-learns an initialization from reference models to enable accurate, label-free performance estimation for unseen models across architectures and modalities.
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2026 2verdicts
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Diff-SBSR uses a frozen Stable Diffusion backbone enhanced by multimodal CLIP and BLIP features plus Circle-T loss to outperform prior methods on zero-shot sketch-based 3D shape retrieval benchmarks.
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Learning to Evaluate: Cost-Effective Model Evaluation on Unlabeled Data with Meta-Learning
MetaEvaluator meta-learns an initialization from reference models to enable accurate, label-free performance estimation for unseen models across architectures and modalities.
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Diff-SBSR: Learning Multimodal Feature-Enhanced Diffusion Models for Zero-Shot Sketch-Based 3D Shape Retrieval
Diff-SBSR uses a frozen Stable Diffusion backbone enhanced by multimodal CLIP and BLIP features plus Circle-T loss to outperform prior methods on zero-shot sketch-based 3D shape retrieval benchmarks.