FIKA-Bench: From Fine-grained Recognition to Fine-Grained Knowledge Acquisition
Fine-grained recognition in everyday life is often not a closed-book classification problem: when encountering unfamiliar objects, humans actively search, compare visual details, and verify evidence before deciding. Existing benchmarks primarily evaluate visually recognition, leaving this active external knowledge acquisition ability underexplored. We study fine-grained knowledge acquisition, where a system must seek, verify, and use external evidence to answer open-ended fine-grained recognition questions. We introduce FIKA-Bench, a leakage-aware and evidence-grounded collection of 311 public-source and real-life instances. To ensure high quality, every example is filtered against fronti...
| Authors | Geng Li, Yuxin Peng |
|---|---|
| Primary category | cs.CV |
| Published | 13 May 2026 |
| Last updated at source | 19 May 2026 |
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