Author

Jin-Ge Ma

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#artificial intelligence Review Oct 2026

Improving Image-Based Nutrition Estimation Through Multimodal Food-Item Verification and Recovery

Single-image nutrition estimation can fail silently when visible foods are missed. Even when a food is correctly identified, its proposed region may not support portion estimation. We propose a framework that uses multimodal large language models (MLLMs) to inventory visible foods and separately verify food identity an...

Jing-Bo Yue, Bruce Coburn, Jin-Ge Ma et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Mitigating Performance Discrepancy in Cross-Domain 3D Class-Incremental Learning

PolyMem, an exemplar-free approach that implicitly models rich high-order statistics of the feature distribution to enhance cross-domain robustness, is introduced that effectively alleviates the performance discrepancy while improving the model's performance across domains.

Jin-Ge Ma, Gautham Vinod, Bruce Coburn et al. · 0 citations

Jul 2026

Open-KNEAD: Knowledge-grounded Nutrition Estimation via Agentic Decomposition

Open-KNEAD is introduced, a knowledge-grounded agentic framework for meal nutrition estimation that is training-free and locally deployable, and improves portion estimates over both prior grounding methods and direct estimation in most backbone-dataset settings.

Bruce Coburn, Jing-Bo Yue, Jin-Ge Ma et al. · arXiv.org · 0 citations