Fixing Imbalanced Attention to Mitigate In-Context Hallucination of
  Large Vision-Language Model
Large Vision Language Models (LVLMs) have demonstrated remarkable
capabilities in understanding and describing visual content, achieving
state-of-the-art performance across various vision-language tasks. However,
these models frequently exhibit hallucination behavior, where they generate
descriptions containing objects or details absent in the input image. Our work
investigates this phenomenon by analyzing attention patterns across transformer
layers and heads, revealing that hallucinations often stem from progressive
degradation of visual grounding in deeper layers. We propose a novel attention
modification approach that combines selective token emphasis and head-specific
modulation to maintain visual grounding throughout the generation process. Our
method introduces two key components: (1) a dual-stream token selection
mechanism that identifies and prioritizes both locally informative and
spatially significant visual tokens, and (2) an attention head-specific
modulation strategy that differentially amplifies visual information processing
based on measured visual sensitivity of individual attention heads. Through
extensive experimentation on the MSCOCO dataset, we demonstrate that our
approach reduces hallucination rates by up to 62.3\% compared to baseline
models while maintaining comparable task performance. Our analysis reveals that
selectively modulating tokens across attention heads with varying levels of
visual sensitivity can significantly improve visual grounding without requiring
model retraining.
67914269013258a9daeec92a
Kazi Hasan Ibn Arif
extracted_confirmed
2025-01-22T19:35:08.167Z
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Hasan Arif
user
hasanar1f
67914269013258a9daeec92b
Sajib Acharjee Dip
67914269013258a9daeec92c
Khizar Hussain
extracted_confirmed
2025-01-22T19:24:54.932Z
6719304706ed69d813c17f72
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Khizar Husasin
user
KhizarH
67914269013258a9daeec92d
Lang Zhang
extracted_confirmed
2025-01-22T22:27:51.230Z
677d52aeb04a44ce6218c6ab
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/O_co9rCqruMMlyoqXn1vS.png
Lang Zhang
user
Lang2000
67914269013258a9daeec92e
Chris Thomas
Fixing Imbalanced Attention to Mitigate In-Context Hallucination of
  Large Vision-Language Model
Large Vision Language Models (LVLMs) have demonstrated remarkable
capabilities in understanding and describing visual content, achieving
state-of-the-art performance across various vision-language tasks. However,
these models frequently exhibit hallucination behavior, where they generate
descriptions containing objects or details absent in the input image. Our work
investigates this phenomenon by analyzing attention patterns across transformer
layers and heads, revealing that hallucinations often stem from progressive
degradation of visual grounding in deeper layers. We propose a novel attention
modification approach that combines selective token emphasis and head-specific
modulation to maintain visual grounding throughout the generation process. Our
method introduces two key components: (1) a dual-stream token selection
mechanism that identifies and prioritizes both locally informative and
spatially significant visual tokens, and (2) an attention head-specific
modulation strategy that differentially amplifies visual information processing
based on measured visual sensitivity of individual attention heads. Through
extensive experimentation on the MSCOCO dataset, we demonstrate that our
approach reduces hallucination rates by up to 62.3\% compared to baseline
models while maintaining comparable task performance. Our analysis reveals that
selectively modulating tokens across attention heads with varying levels of
visual sensitivity can significantly improve visual grounding without requiring
model retraining.
The proposed method reduces hallucinations in Large Vision Language Models by selectively emphasizing tokens and modulating attention heads based on visual sensitivity, maintaining task performance.
Large Vision Language Models LVLMs hallucination behavior attention patterns transformer layers token selection mechanism attention head-specific modulation visual grounding MSCOCO dataset