Pensieve: Retrospect-then-Compare Mitigates Visual Hallucination
Dingchen Yang Bowen Cao Guang Chen Changjun Jiang
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 completely oblivious to accurate visual cues when they hallucinate?
Our investigation reveals that the visual branch may simultaneously advocate
both accurate and non-existent content. To address this issue, we propose
Pensieve, a training-free method inspired by our observation that analogous
visual hallucinations can arise among images sharing common semantic and
appearance characteristics. During inference, Pensieve enables MLLMs to
retrospect relevant images as references and compare them with the test image.
This paradigm assists MLLMs in downgrading hallucinatory content mistakenly
supported by the visual input. Experiments on Whoops, MME, POPE, and LLaVA
Bench demonstrate the efficacy of Pensieve in mitigating visual hallucination,
surpassing other advanced decoding strategies. Additionally, Pensieve aids
MLLMs in identifying details in the image and enhancing the specificity of
image descriptions.