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English(EN) Multi-Modal Cross-Domain Alignment Network for Video Moment Retrieval

新网络改进跨域视频片段检索

研究人员推出了一种新颖的多模态跨域对齐(MMCDA)网络,旨在改进跨不同数据集的视频片段检索。该方法解决了在将在一个领域训练的模型应用于另一个领域时性能下降的挑战,特别是在目标领域缺乏标注的情况下。MMCDA网络结合了域对齐、跨模态对齐和特定的对齐模块,以学习域不变和语义对齐的表示,从而实现从有标注的源域到无标注的目标域的有效知识迁移。 AI

影响 引入了一种改进视频检索任务跨域泛化能力的方法,可能减少在新领域进行大量手动标注的需求。

排序理由 这是一篇描述特定任务新网络的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新网络改进跨域视频片段检索

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这是一篇描述特定任务新网络的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Xiang Fang, Daizong Liu, Pan Zhou, Yuchong Hu ·

    用于视频时刻检索的多模态跨域对齐网络

    arXiv:2209.11572v3 Announce Type: replace-cross Abstract: As an increasingly popular task in multimedia information retrieval, video moment retrieval (VMR) aims to localize the target moment from an untrimmed video according to a given language query. Most previous methods depend…