Mitigating Object Hallucinations in Large Vision-Language Models via Attention Calibration

TL;DR

This paper introduces Dynamic Attention Calibration (DAC), a novel, adaptable method that reduces object hallucinations in large vision-language models by calibrating attention maps to be position-invariant, improving factual alignment.

Contribution

The paper proposes a dynamic, contrastive learning-based attention calibration module that generalizes across models and inputs, effectively mitigating hallucinations in LVLMs.

Findings

DAC significantly reduces object hallucinations.

The method improves multimodal alignment across benchmarks.

DAC outperforms static attention reordering methods.

Abstract

Large Vision-Language Models (LVLMs) exhibit impressive multimodal reasoning capabilities but remain highly susceptible to object hallucination, where models generate responses that are not factually aligned with the visual content. Recent works attribute this issue to an inherent bias of LVLMs where the vision token attention map has spurious focus on certain positions, and propose to mitigate this issue by reordering visual tokens. However, we find that different LVLMs exhibit different correlations between attention and spatial position, which makes existing static solutions difficult to generalize to other LVLMs. To begin with, we investigate the attention bias introduced by image tokens through a toy experiment, in which a blank image is fed into the model to capture its position-dependent bias. We then remove this bias from the original attention map, which already leads to a substantial reduction in hallucinations. This proof of concept validates the core intuition behind attention calibration. Building upon this insight, we propose Dynamic Attention Calibration (DAC), a lightweight, plug-and-play module that leverages contrastive learning to dynamically enforce positional invariance. Unlike static baselines, DAC adapts to different models and inputs in a robust and learnable manner, offering a generalizable solution to mitigate attention-related hallucinations in LVLMs. Comprehensive experiments across multiple benchmarks demonstrate that DAC significantly reduces object hallucination while improving general multimodal alignment. Our method achieves state-of-the-art performance across diverse LVLM architectures on various metrics. Our code is available at https://github.com/johnnyzyn/attention-calibration.