arXiv Computer Vision
Attention at Rest Stays at Rest: Breaking Visual Inertia to Mitigate Relation Hallucinations
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arXiv AI
Aug 10
Same Attention, Different Truths: Put Logit-Lens over Visual Attention to Detect and Mitigate LVLM Object Hallucination
arXiv:2608. 07302v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) often suffer from object hallucination, generating objects that are absent from the image.
arXiv AI
Jun 16
Mitigating Object Hallucinations in LVLMs via Attention Imbalance Rectification
arXiv:2603. 24058v2 Announce Type: replace-cross Abstract: Object hallucination in Large Vision-Language Models (LVLMs) severely compromises their reliability in real-world applications, posing a critical barrier to their deployment in high-stakes scenarios such as autonomous driving and medical image analysis.
arXiv Computer Vision
Sep 16
What Do Hallucinations Reveal About Multimodal Reasoning? Diagnosing Visual Grounding Failures via Contrastive Decoding Probes
arXiv:2609.16646v1 Announce Type: new Abstract: When strong multimodal models are widely available, progress requires new scientific methodologies beyond benchmark scores---using models as instrument...
arXiv AI
Jul 1
ADAPT: Attention Dynamics Alignment with Preference Tuning for Faithful MLLMs
arXiv:2606. 31054v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) are critically hampered by hallucination, generating content inconsistent with the provided image.
arXiv AI
Oct 9
Beyond Visual Enhancement: Adaptive Multi-Context Steering to Mitigate LVLM Hallucinations
The paper introduces AIMS (Adaptive Information Multi-source Steering), a lightweight, training‑free framework that dynamically balances visual, prefilled textual, and generated contexts during decoding to reduce hallucinations in Large Vision‑Language Models (LVLMs). By constructing compact prototypes for each context domain and estimating their affinities with the current query, AIMS assigns head‑wise steering weights that guide adaptive context integration without extra training or forward passes. Experiments across multiple LVLMs and decoding strategies show that AIMS effectively mitigates object hallucination while preserving general multimodal performance.
arXiv AI
Jun 4
Correcting Visual Blur Induced by Attention Distraction to Reduce Hallucinations: Algorithm and Theory
arXiv:2605. 24602v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) frequently suffer from object hallucinations, yet the visual perceptual mechanism underlying this failure remains poorly understood.