HCVP:利用层次对比视觉提示实现领域泛化

IF 8.4 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Guanglin Zhou;Zhongyi Han;Shiming Chen;Biwei Huang;Liming Zhu;Tongliang Liu;Lina Yao;Kun Zhang
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引用次数: 0

摘要

领域泛化(DG)致力于通过学习不变特征来创建在未知场景中表现出色的机器学习模型。在DG中,将模型约束为固定结构或统一参数化以封装不变特征的普遍做法可能会无意中混合特定方面。这种方法与领域间变化的细微差别作斗争,并可能对某些领域表现出偏见,阻碍了对领域不变特征的精确学习。认识到这一点,我们引入了一种新的方法,旨在用领域级和任务特定的特征来补充模型。该方法旨在指导模型更有效地将不变特征与特定特征分离开来,从而提高模型的泛化程度。基于DG范式中视觉提示的新兴趋势,我们的工作引入了新的分层对比视觉提示(HCVP)方法。这代表了该领域的重大进步,它以独特的生成方法来提示,以及明确的模型结构和专门的损失函数来区分自己。与通常在整个数据集共享的传统视觉提示不同,HCVP利用分层提示生成网络,通过提示对比学习增强。这些生成提示是依赖于实例的,迎合不同领域和任务固有的独特特征。此外,我们设计了一个提示调制网络作为桥梁,有效地将生成的视觉提示合并到视觉变压器主干中。在五个DG数据集上进行的实验证明了HCVP的有效性,优于现有的DG算法和自适应协议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization
Domain Generalization (DG) endeavors to create machine learning models that excel in unseen scenarios by learning invariant features. In DG, the prevalent practice of constraining models to a fixed structure or uniform parameterization to encapsulate invariant features can inadvertently blend specific aspects. Such an approach struggles with nuanced differentiation of inter-domain variations and may exhibit bias towards certain domains, hindering the precise learning of domain-invariant features. Recognizing this, we introduce a novel method designed to supplement the model with domain-level and task-specific characteristics. This approach aims to guide the model in more effectively separating invariant features from specific characteristics, thereby boosting the generalization. Building on the emerging trend of visual prompts in the DG paradigm, our work introduces the novel Hierarchical Contrastive Visual Prompt (HCVP) methodology. This represents a significant advancement in the field, setting itself apart with a unique generative approach to prompts, alongside an explicit model structure and specialized loss functions. Differing from traditional visual prompts that are often shared across entire datasets, HCVP utilizes a hierarchical prompt generation network enhanced by prompt contrastive learning. These generative prompts are instance-dependent, catering to the unique characteristics inherent to different domains and tasks. Additionally, we devise a prompt modulation network that serves as a bridge, effectively incorporating the generated visual prompts into the vision transformer backbone. Experiments conducted on five DG datasets demonstrate the effectiveness of HCVP, outperforming both established DG algorithms and adaptation protocols.
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来源期刊
IEEE Transactions on Multimedia
IEEE Transactions on Multimedia 工程技术-电信学
CiteScore
11.70
自引率
11.00%
发文量
576
审稿时长
5.5 months
期刊介绍: The IEEE Transactions on Multimedia delves into diverse aspects of multimedia technology and applications, covering circuits, networking, signal processing, systems, software, and systems integration. The scope aligns with the Fields of Interest of the sponsors, ensuring a comprehensive exploration of research in multimedia.
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