arXiv Computer Vision September 3, 2026 By Xuanbing Wen, Boxu Chen, Le Yang, Jiakai Wang, Zhengyu Zhao, Chenhao Lin, Chao Shen

Detecting Object Hallucinations in Large Vision-Language Models via Cross-Modal Attention Drifts and Mask-Based Verification

Read the original on arXiv Computer Vision →

The paper introduces CADMP, a lightweight framework for detecting object hallucinations in large vision‑language models. CADMP measures cross‑modal attention drift between adjacent layers and verifies predictions by masking visually relevant regions, combining these signals to identify hallucinated outputs. Experiments on multiple benchmarks show that CADMP achieves competitive detection performance, and ablation studies confirm the complementary roles of attention drift and mask‑based verification.

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