RealBirdID: Benchmarking Bird Species Identification in the Era of MLLMs
Fine-grained bird species identification in the wild is frequently unanswerable from a single image: key cues may be non-visual (e.g. vocalization), or obscured due to occlusion, camera angle, or low resolution. Yet today's multimodal systems are typically judged on answerable, in-schema cases, encouraging confident guesses rather than principled abstention. We propose the RealBirdID benchmark: given an image of a bird, a system should either answer with a species or abstain with a concrete, evidence-based rationale: "requires vocalization," "low quality image," or "view obstructed". For each genus, the dataset includes a validation split composed of curated unanswerable examples with lab...
| Authors | Logan Lawrence, Mustafa Chasmai, Rangel Daroya et al. |
|---|---|
| Primary category | cs.CV |
| Published | 27 March 2026 |
| Last updated at source | 27 March 2026 |
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