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September 20, 2025

Understanding Visual Detail Hallucinations of Large Vision-Language Models

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LVLMs show decreased performance in recognizing small objects compared to regular-sized ones, underscoring critical limitations.
Three training-free methods were evaluated, with variable success in improving performance on fine-grained visual hallucination tasks.
Ablation studies highlight that ConvNeXt architecture is essential for tasks related to object existence recognition.
Integrating information from multiple visual encoders proves to be more effective in minimizing certain hallucinations.

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This research assesses visual hallucinations in LVLMs, revealing performance challenges with small objects.

Sun et al. (2025) studied this question.

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