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2024

Pensieve: Retrospect-then-Compare Mitigates Visual Hallucination

Yang, Dingchen, Cao, Bowen, Chen, Guang et al.

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Multi-modal Large Language Models (MLLMs) demonstrate remarkable success across various vision-language tasks.

However, they suffer from visual hallucination, where the generated responses diverge from the provided image.
Are MLLMs oblivious to the accurate visual cues when they hallucinate? Our investigation reveals that the visual branch may equally advocate both accurate and erroneous content.
To address this issue, we propose Pensieve, a training-free method that leverages the analogous visual hallucinations, which are induced by images sharing common semantic and appearance characteristics, to mitigate hallucination.

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