Toward Low-Burden Image-Based Dietary Assessment Through Human-AI Collaborative Correction
4.40T1 sourcearXiv cs.HC
Source record
Published by arXiv cs.HC (T1 source). The original is at https://arxiv.org/abs/2610.41300.
Pipeline notes
The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.
SummaryA within-participant study of 30 users tests an intent-driven correction workflow for image-based dietary logs. Users state corrections by text, voice, or guided prompts; the system regenerates the linked record for verification. The approach reduced measured workload, completion time, clicks, and preference favored it.
Why it mattersProvides measured evidence for designing agentic correction flows in which users specify desired changes and agents regenerate structured records for final review.
Cited by
No citations on record.