Mitigating Object Hallucination in Large Vision-Language Models via False Discovery Controlled Visual Data Splitting
Multiple object hallucination, where large vision-language models (LVLMs) generate objects not supported by the visual input, is a persistent challenge caused by visual uncertainty during decoding. Existing methods reduce hallucinations using contrastive signals, but they rely on heuristics and lack principled control of false positives at the image level. To address this, we propose False Discovery Rate-COntRol of HALlucination (CORAL), a training-free framework that models visual uncertainty using an uncertainty-aware visual data splitting strategy and leverages mirror statistics to quantify visual contrast during decoding. By computing mirror statistics from paired, symmetrically pertu...
| Authors | Chang Liu, Yu Tian, Rui Xie |
|---|---|
| Primary category | cs.CV |
| Published | 30 September 2026 |
| Last updated at source | 1 October 2026 |
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