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, yielding 252 items across 12 topical categories and 7 user intents. Each item includes a concise refe...
| Authors | Haonan Jiang, Guojian Zhan, Jiancong Xie et al. |
|---|---|
| Primary category | cs.AI |
| Published | 14 September 2026 |
| Last updated at source | 20 September 2026 |
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