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

arXiv:2610.41300v1 Announce Type: new 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