OCCO：基于对象感知与上下文对比学习的LVM引导红外与可见光图像融合框架
OCCO: LVM-guided Infrared and Visible Image Fusion Framework based on Object-aware and Contextual COntrastive Learning
AI总结：
针对现有图像融合方法难以兼顾图像质量与下游任务性能的问题，提出LVM引导的OCCO融合框架，结合对象感知与上下文对比学习及特征交互融合网络，在4个数据集上优于8种SOTA方法，且提升下游任务性能。
摘要：
图像融合是计算机视觉领域的一项关键技术，其目标是生成高质量的融合图像并提升下游任务的性能。然而，现有融合方法难以在这两个因素之间取得平衡：追求融合图像的高质量可能导致下游视觉任务性能下降，反之亦然。为解决这一缺陷，本文提出了一种新型的由LVM（大视觉模型）引导、结合对象感知与上下文对比学习的融合框架，命名为OCCO。该框架利用预训练LVM提供语义引导，使网络能够专注于融合任务，同时以对比学习的方式强化对显著语义特征的学习。此外，本文还设计了一种新型的特征交互融合网络，用于解决因模态差异导致的融合图像信息冲突问题。通过在潜在特征空间（上下文空间）中学习正样本与负样本的差异，该方法提升了融合图像中目标信息的完整性，从而优化下游任务性能。最后，本文在4个数据集上与8种最先进的方法进行了对比，验证了所提方法的有效性，同时证明其在下游视觉任务上也表现出优异性能。
英文摘要：
Image fusion is a crucial technique in the field of computer vision, and its goal is to generate high-quality fused images and improve the performance of downstream tasks. However, existing fusion methods struggle to balance these two factors. Achieving high quality in fused images may result in lower performance in downstream visual tasks, and vice versa. To address this drawback, a novel LVM (large vision model)-guided fusion framework with Object-aware and Contextual COntrastive learning is proposed, termed as OCCO. The pre-trained LVM is utilized to provide semantic guidance, allowing the network to focus solely on fusion tasks while emphasizing learning salient semantic features in form of contrastive learning. Additionally, a novel feature interaction fusion network is also designed to resolve information conflicts in fusion images caused by modality differences. By learning the distinction between positive samples and negative samples in the latent feature space (contextual space), the integrity of target information in fused image is improved, thereby benefiting downstream performance. Finally, compared with eight state-of-the-art methods on four datasets, the effectiveness of the proposed method is validated, and exceptional performance is also demonstrated on downstream visual task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
