CAST: Mitigating Object Hallucination in Large Vision-Language Models via Caption-Guided Visual Attention Steering

• URL: http://arxiv.org/abs/2605.04641v1
• Date: Wed, 06 May 2026 08:32:30 GMT
• Title: CAST: Mitigating Object Hallucination in Large Vision-Language Models via Caption-Guided Visual Attention Steering
• Abstract summary: Large Vision-Language Models (LVLMs) produce contents that deviate from visual information, leading to object hallucination.<n>We propose Caption-guided Visual Attention Steering (CAST), a training-free, plug-and-play hallucination mitigation method.<n>CAST reduces object hallucination by an average of 6.03% across five widely used LVLMs and five benchmarks.
• Score: 88.89529097037162
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although Large Vision-Language Models (LVLMs) have demonstrated remarkable performance on downstream tasks, they frequently produce contents that deviate from visual information, leading to object hallucination. To tackle this, recent works mostly depend on expensive manual annotations and training cost, or decoding strategies which significantly increase inference time. In this work, we observe that LVLMs' attention to visual information is significantly enhanced when answering caption queries compared to non-caption queries. Inspired by this phenomenon, we propose Caption-guided Visual Attention Steering (CAST), a training-free, plug-and-play hallucination mitigation method that leverages the attention activation pattern corresponding to caption queries to enhance LVLMs' visual perception capability. Specifically, we use probing techniques to identify attention heads that are highly sensitive to caption queries and estimate optimized steering directions for their outputs. This steering strengthens LVLM's fine-grained visual perception capabilities, thereby effectively mitigating object hallucination. CAST reduced object hallucination by an average of 6.03% across five widely used LVLMs and five benchmarks including both discriminative and generative tasks, demonstrating state-of-the-art performance while adding little inference cost and preserving other foundational capabilities.

Related papers

• When Prompts Override Vision: Prompt-Induced Hallucinations in LVLMs [54.411658510110215]
  We propose HalluScope to better understand the extent to which different factors induce hallucinations.<n>HalluVL-DPO is a framework for fine-tuning off-the-shelf LVLMs towards more visually grounded responses.
  arXiv  Detail & Related papers  (2026-04-23T17:54:36Z)
• Tell Model Where to Look: Mitigating Hallucinations in MLLMs by Vision-Guided Attention [12.491288365811657]
  Vision-Guided Attention (VGA) is a training-free method that first constructs precise visual grounding by exploiting the semantic content of visual tokens.<n>In image captioning, VGA further refines this guidance dynamically during generation by suppressing regions that have already been described.<n>In VGA, each token undergoes only a single forward pass, introducing a negligible latency overhead of just 4.36%.
  arXiv  Detail & Related papers  (2025-11-25T07:58:57Z)
• Modality Bias in LVLMs: Analyzing and Mitigating Object Hallucination via Attention Lens [0.0]
  Large vision-language models (LVLMs) have demonstrated remarkable multimodal comprehension and reasoning capabilities.<n>LVLMs tend to over-rely on textual prompts and internal knowledge of large language models, generating descriptions inconsistent with visual cues.<n>We propose a training-free method to mitigate object hallucination.
  arXiv  Detail & Related papers  (2025-08-04T13:40:59Z)
• CAI: Caption-Sensitive Attention Intervention for Mitigating Object Hallucination in Large Vision-Language Models [60.0300765815417]
  Large Vision-Language Models (LVLMs) frequently produce content that deviates from visual information, leading to object hallucination.<n>We propose Caption-sensitive Attention Intervention (CAI), a training-free, plug-and-play hallucination mitigation method.
  arXiv  Detail & Related papers  (2025-06-30T07:52:36Z)
• Cracking the Code of Hallucination in LVLMs with Vision-aware Head Divergence [69.86946427928511]
  We investigate the internal mechanisms driving hallucination in large vision-language models (LVLMs)<n>We introduce Vision-aware Head Divergence (VHD), a metric that quantifies the sensitivity of attention head outputs to visual context.<n>We propose Vision-aware Head Reinforcement (VHR), a training-free approach to mitigate hallucination by enhancing the role of vision-aware attention heads.
  arXiv  Detail & Related papers  (2024-12-18T15:29:30Z)
• Visual Description Grounding Reduces Hallucinations and Boosts Reasoning in LVLMs [52.497823009176074]
  Large Vision-Language Models (LVLMs) often produce responses that misalign with factual information, a phenomenon known as hallucinations.<n>We introduce Visual Description Grounded Decoding (VDGD), a training-free method designed to enhance visual perception and improve reasoning capabilities in LVLMs.
  arXiv  Detail & Related papers  (2024-05-24T16:21:59Z)
• Mitigating Object Hallucination in Large Vision-Language Models via Image-Grounded Guidance [51.30560006045442]
  Image-gRounded guIdaNcE (MARINE) is a framework that is both training-free and API-free.<n>MARINE effectively and efficiently reduces object hallucinations during inference by introducing image-grounded guidance to LVLMs.<n>Our framework's flexibility further allows for the integration of multiple vision models, enabling more reliable and robust object-level guidance.
  arXiv  Detail & Related papers  (2024-02-13T18:59:05Z)

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.