SOURCE-LINKED INTELLIGENCE
NoteVQA: Benchmarking VLMs on Real-Life Questions from Human Communities
Vision-language models (VLMs) increasingly power consumer-facing AI search, yet evaluating them on the diversity of everyday visual questions remains challenging. Existing benchmarks often target predefined capabilities, such as multi-hop retrieval or long-form synthesis, whereas users ask photo-grounded questions spanning a long tail of everyday scenarios. Despite advances in VLMs, users on Xiaohongshu, a mainstream Chinese image-sharing platform, continue to turn to other people for help with everyday visual questions. Motivated by this behaviour, we curate NoteVQA from these questions, yiel
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-14T15:01:10.000Z
First collected: 2026-09-20T09:41:04.278Z. This is not the publication date.