Watch Closely: Mitigating Object Hallucinations in Large Vision-Language Models with Disentangled Decoding

• URL: http://arxiv.org/abs/2512.19070v1
• Date: Mon, 22 Dec 2025 06:20:53 GMT
• Title: Watch Closely: Mitigating Object Hallucinations in Large Vision-Language Models with Disentangled Decoding
• Authors: Ruiqi Ma, Yu Yan, Chunhong Zhang, Minghao Yin, XinChao Liu, Zhihong Jin, Zheng Hu,
• Abstract summary: Large Vision-Language Models (LVLMs) bridge the gap between visual and linguistic modalities.<n>These models often fail to accurately identify certain objects, leading to text generation that appears fluent but does not correspond to the visual content.<n>We introduce Hallucination Disentangled Decoding (HDD) method that requires no training.
• Score: 22.560247372346435
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Vision-Language Models (LVLMs) bridge the gap between visual and linguistic modalities, demonstrating strong potential across a variety of domains. However, despite significant progress, LVLMs still suffer from severe hallucination issues in object recognition tasks. These models often fail to accurately identify certain objects, leading to text generation that appears fluent but does not correspond to the visual content, which can have serious consequences in real-world applications. Recently, several methods have been proposed to alleviate LVLM hallucinations, but most focus solely on reducing hallucinations in the language modality. To mitigate hallucinations in both the language and visual modalities, we introduce Hallucination Disentangled Decoding (HDD) method that requires no training. HDD enhances the original image by segmenting it and selecting images that augment the original, while also utilizing a blank image to eliminate language prior hallucinations in both the original and segmented images. This design not only reduces the model's dependence on language priors but also enhances its visual performance. (Code: https://github.com/rickeyhhh/Hallucination-Disentangled-Decoding)

Related papers

• NoLan: Mitigating Object Hallucinations in Large Vision-Language Models via Dynamic Suppression of Language Priors [54.688164483265496]
  We propose No-Language-Hallucination Decoding, NoLan, which refines the output distribution by dynamically suppressing language priors.<n>NoLan achieves substantial improvements on POPE, enhancing the accuracy of LLaVA-1.5 7B and Qwen-VL 7B by up to 6.45 and 7.21, respectively.
  arXiv  Detail & Related papers  (2026-02-25T17:50:41Z)
• Residual Decoding: Mitigating Hallucinations in Large Vision-Language Models via History-Aware Residual Guidance [31.7541034166056]
  Large Vision-Language Models (LVLMs) can reason effectively from image-text inputs and perform well in various multimodal tasks.<n>They are affected by language priors and often produce hallucinations.<n>We propose Residual Decoding (ResDec) to address this problem.
  arXiv  Detail & Related papers  (2026-02-01T06:12:05Z)
• VEGAS: Mitigating Hallucinations in Large Vision-Language Models via Vision-Encoder Attention Guided Adaptive Steering [5.541436522468184]
  Large vision-language models (LVLMs) produce outputs that are linguistically fluent but factually inconsistent with the visual evidence.<n>We show that LVLMs tend to hallucinate when their final visual-attention maps fail to concentrate on key image objects.<n>We introduce VEGAS, a method that integrates the vision encoder's attention maps into the language model's mid-layers and adaptively steers tokens which fail to concentrate on key image objects.
  arXiv  Detail & Related papers  (2025-12-12T23:33:50Z)
• VaLiD: Mitigating the Hallucination of Large Vision Language Models by Visual Layer Fusion Contrastive Decoding [38.23310445372371]
  Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in multimodal task reasoning.<n>They often generate responses that appear plausible yet do not accurately reflect the visual content, a phenomenon known as hallucination.<n>Recent approaches have introduced training-free methods to mitigate hallucinations by adjusting the decoding strategy during the inference stage.<n>We propose a novel hallucination-mitigation method from the visual encoding perspective: textbfVisutextbfal textbfLayer Fustextbfion Contrastive textbfD
  arXiv  Detail & Related papers  (2024-11-24T13:42:02Z)
• HELPD: Mitigating Hallucination of LVLMs by Hierarchical Feedback Learning with Vision-enhanced Penalty Decoding [36.360171373963716]
  Large Vision-Language Models (LVLMs) have shown remarkable performance on many visual-language tasks. These models still suffer from multimodal hallucination, which means the generation of objects or content that violates the images. We propose Hierarchical Feedback Learning with Vision-enhanced Penalty Decoding (HELPD) to address this issue.
  arXiv  Detail & Related papers  (2024-09-30T15:52:05Z)
• Mitigating Multilingual Hallucination in Large Vision-Language Models [35.75851356840673]
  We propose a two-stage Multilingual Hallucination Removal (MHR) framework for Large Vision-Language Models (LVLMs) Instead of relying on the intricate manual annotations of multilingual resources, we propose a novel cross-lingual alignment method. Our framework delivers an average increase of 19.0% in accuracy across 13 different languages.
  arXiv  Detail & Related papers  (2024-08-01T13:34:35Z)
• Does Object Grounding Really Reduce Hallucination of Large Vision-Language Models? [53.89380284760555]
  Large vision-language models (LVLMs) produce captions that mention concepts that cannot be found in the image. These hallucinations erode the trustworthiness of LVLMs and are arguably among the main obstacles to their ubiquitous adoption. Recent work suggests that addition of grounding objectives -- those that explicitly align image regions or objects to text spans -- reduces the amount of LVLM hallucination.
  arXiv  Detail & Related papers  (2024-06-20T16:56:11Z)
• AutoHallusion: Automatic Generation of Hallucination Benchmarks for Vision-Language Models [91.78328878860003]
  Large vision-language models (LVLMs) are prone to hallucinations. benchmarks often rely on hand-crafted corner cases whose failure patterns may not generalize well. We develop AutoHallusion, the first automated benchmark generation approach.
  arXiv  Detail & Related papers  (2024-06-16T11:44:43Z)
• Hallucination Augmented Contrastive Learning for Multimodal Large Language Model [53.65682783591723]
  Multi-modal large language models (MLLMs) have been shown to efficiently integrate natural language with visual information to handle multi-modal tasks. However, MLLMs still face a fundamental limitation of hallucinations, where they tend to generate erroneous or fabricated information. In this paper, we address hallucinations in MLLMs from a novel perspective of representation learning.
  arXiv  Detail & Related papers  (2023-12-12T04:05:15Z)
• Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding [125.05295513481035]
  We introduce Visual Contrastive Decoding (VCD), a simple and training-free method that contrasts output distributions derived from original and distorted visual inputs. The proposed VCD effectively reduces the over-reliance on statistical bias and unimodal priors, two essential causes of object hallucinations. Our experiments show that VCD, without either additional training or the usage of external tools, significantly mitigates the object hallucination issue across different LVLM families.
  arXiv  Detail & Related papers  (2023-11-28T16:26:35Z)
• Analyzing and Mitigating Object Hallucination in Large Vision-Language Models [110.12460299261531]
  Large vision-language models (LVLMs) have shown remarkable abilities in understanding visual information with human languages. LVLMs still suffer from object hallucination, which is the problem of generating descriptions that include objects that do not actually exist in the images. We propose a powerful algorithm, LVLM Hallucination Revisor (LURE), to rectify object hallucination in LVLMs by reconstructing less hallucinatory descriptions.
  arXiv  Detail & Related papers  (2023-10-01T18:10:53Z)
• Plausible May Not Be Faithful: Probing Object Hallucination in Vision-Language Pre-training [66.0036211069513]
  Large-scale vision-language pre-trained models are prone to hallucinate non-existent visual objects when generating text. We show that models achieving better scores on standard metrics could hallucinate objects more frequently. Surprisingly, we find that patch-based features perform the best and smaller patch resolution yields a non-trivial reduction in object hallucination.
  arXiv  Detail & Related papers  (2022-10-14T10:27:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.