Systematic zero-shot benchmarking of open-source VLMs on multimodal grocery product retrieval shows data quality outperforms scale, introduces semantic power density as an efficiency metric, and identifies a persistent top-1 precision gap.
In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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JEPAMatch augments FlexMatch with LeJEPA-derived latent regularization to produce better-structured representations, yielding higher accuracy and faster convergence on CIFAR-100, STL-10, and Tiny-ImageNet.
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What Matters for Grocery Product Retrieval with Open Source Vision Language Models
Systematic zero-shot benchmarking of open-source VLMs on multimodal grocery product retrieval shows data quality outperforms scale, introduces semantic power density as an efficiency metric, and identifies a persistent top-1 precision gap.
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JEPAMatch: Geometric Representation Shaping for Semi-Supervised Learning
JEPAMatch augments FlexMatch with LeJEPA-derived latent regularization to produce better-structured representations, yielding higher accuracy and faster convergence on CIFAR-100, STL-10, and Tiny-ImageNet.