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/CVF conference on computer vision and pattern recognition
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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.