利用知识注入学习提升众筹成功预测的跨模态上下文一致性
Enhancing Cross-Modal Contextual Congruence for Crowdfunding Success using Knowledge-infused Learning
AI总结：
本研究引入知识图谱的外部常识知识，结合紧凑型视觉语言模型增强多模态数据表征，以提升众筹活动成功预测性能，验证了跨模态上下文一致性对众筹成功的重要性。
摘要：
数字领域随着多模态技术不断演进，丰富了用户的在线体验。创作者和营销人员旨在将来自不同模态的细微上下文线索融入一致的内容中，以和谐的信息吸引用户。这种多模态线索的相互作用通常是吸引用户注意力的关键因素。然而，多模态的丰富性给计算建模带来了挑战，因为需要统一跨模态的语义上下文线索，以捕捉多模态内容真正的整体含义。这种上下文含义在吸引用户参与方面至关重要，因为它传达了品牌或组织的预期信息。在本研究中，我们引入来自知识图谱的外部常识知识，使用紧凑型视觉语言模型（VLM）增强多模态数据的表征，并预测多模态众筹活动的成功。我们的结果表明，外部常识知识弥合了文本与图像模态之间的语义鸿沟，增强后的知识注入表征相较于无知识的基线模型，提升了活动成功预测的性能。我们的发现凸显了在线多模态内容中的上下文一致性对于打造有吸引力且成功的众筹活动的重要性。
英文摘要：
The digital landscape continually evolves with multimodality, enriching the online experience for users. Creators and marketers aim to weave subtle contextual cues from various modalities into congruent content to engage users with a harmonious message. This interplay of multimodal cues is often a crucial factor in attracting users' attention. However, this richness of multimodality presents a challenge to computational modeling, as the semantic contextual cues spanning across modalities need to be unified to capture the true holistic meaning of the multimodal content. This contextual meaning is critical in attracting user engagement as it conveys the intended message of the brand or the organization. In this work, we incorporate external commonsense knowledge from knowledge graphs to enhance the representation of multimodal data using compact Visual Language Models (VLMs) and predict the success of multi-modal crowdfunding campaigns. Our results show that external knowledge commonsense bridges the semantic gap between text and image modalities, and the enhanced knowledge-infused representations improve the predictive performance of models for campaign success upon the baselines without knowledge. Our findings highlight the significance of contextual congruence in online multimodal content for engaging and successful crowdfunding campaigns.
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。
