Mitigating Object Hallucinations in MLLMs via Multi-Frequency Perturbations

• URL: http://arxiv.org/abs/2503.14895v1
• Date: Wed, 19 Mar 2025 04:39:45 GMT
• Title: Mitigating Object Hallucinations in MLLMs via Multi-Frequency Perturbations
• Authors: Shuo Li, Jiajun Sun, Guodong Zheng, Xiaoran Fan, Yujiong Shen, Yi Lu, Zhiheng Xi, Yuming Yang, Wenming Tan, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang,
• Abstract summary: Large language models (MLLMs) have demonstrated remarkable performance in visual tasks.<n>However, the authenticity of the responses generated by MLLMs is often compromised by object hallucinations.<n>We identify that a key cause of these hallucinations is the model's over-susceptibility to specific image frequency features in detecting objects.
• Score: 44.83933994734478
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, multimodal large language models (MLLMs) have demonstrated remarkable performance in visual-language tasks. However, the authenticity of the responses generated by MLLMs is often compromised by object hallucinations. We identify that a key cause of these hallucinations is the model's over-susceptibility to specific image frequency features in detecting objects. In this paper, we introduce Multi-Frequency Perturbations (MFP), a simple, cost-effective, and pluggable method that leverages both low-frequency and high-frequency features of images to perturb visual feature representations and explicitly suppress redundant frequency-domain features during inference, thereby mitigating hallucinations. Experimental results demonstrate that our method significantly mitigates object hallucinations across various model architectures. Furthermore, as a training-time method, MFP can be combined with inference-time methods to achieve state-of-the-art performance on the CHAIR benchmark.

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