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arXiv · 2610.41300

Toward Low-Burden Image-Based Dietary Assessment Through Human-AI Collaborative Correction

Darren Liu·Andrew Lu·Zhiyuan "Ryan" Wang·Hanqi Luo·Xiao Hu·Runze Yan
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Abstract

Image-based dietary assessment can automate initial food logging, but meal photographs often leave food identity, hidden ingredients, preparation, and portion uncertain, making correction a recurring part of use. We introduce Human-AI Collaborative Correction, which reframes correction as communicate intent $\rightarrow$ regenerate $\rightarrow$ verify: users express what should change through text, voice, or guided clarification, the AI regenerates the linked dietary record, and users verify the result. In a within-participant study with 30 participants, compared with conventional Manual Search-and-Edit Correction, collaborative correction was associated with lower workload (Raw TLX: 35.83 vs. 55.00; p < .001), 21.0% lower recorded usage time, 51.1% fewer recorded clicks, and more favorable user-experience ratings; 90.0% preferred it overall. An exploratory nutrient analysis also found lower energy MAE. These findings support intent-driven regeneration as a promising interaction pattern for reducing correction burden while preserving user verification and making image-based dietary tracking easier to sustain.

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Darren Liu, Andrew Lu, Zhiyuan "Ryan" Wang, Hanqi Luo, Xiao Hu, Runze Yan. 2026-10-30. Toward Low-Burden Image-Based Dietary Assessment Through Human-AI Collaborative Correction. https://arxiv.org/abs/2610.41300

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