Learning from Fine-Grained Visual Discrepancies: Mitigating Multimodal Hallucinations via In-Context Visual Contrastive Optimization
arXiv:2605.31312v1 Announce Type: cross Abstract: Multimodal hallucination remains a persistent challenge for Vision-Language Models (VLMs). Standard textual Direct Preference Optimization (DPO) often fails to mitigate it due to a lack of explicit visual supervision. While existing works introduce visual preference DPO by contrasting original images against negative ones, they suffer from a theoretically inconsistent objective caused by partition function mismatches and rely on coarse-grained negatives that could enable shortcut learning. In this work, we propose In-Context Visual Contrastive
The paper addresses a core limitation of current Vision-Language Models (VLMs) by proposing a novel method to mitigate multimodal hallucinations, a persistent challenge in AI development.
Improving VLM reliability by reducing hallucinations is critical for broader AI application across sensitive domains, enhancing trustworthiness and practical utility.
The proposed 'In-Context Visual Contrastive Optimization' offers a more robust theoretical framework for visual preference optimization than existing DPO methods, potentially leading to more accurate and reliable multimodal AI.
- · AI researchers and developers
- · Companies deploying VLMs
- · Users of multimodal AI applications
- · Developers relying solely on traditional DPO
- · Models prone to severe hallucinations
VLMs will exhibit fewer errors and more coherent responses when processing visual and linguistic information.
Increased trust in AI outputs will accelerate adoption of multimodal AI in critical sectors like healthcare, autonomous systems, and advanced analytics.
The enhanced reliability of VLMs could unlock new applications requiring high fidelity visual understanding, leading to entirely novel AI products and services.
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Read at arXiv cs.CL