Video-Text Retrieval (VTR) is a crucial multi-modal task in an era of massive
video-text data on the Internet. A plethora of work characterized by using a
two-stream Vision-Language model architecture that learns a joint
representation of video-text pairs has become a prominent approach for the VTR
task. However, these models operate un- der the assumption of bijective
video-text correspondences and neglect a more practical scenario where video
content usually encompasses multiple events, while texts like user queries or
webpage metadata tend to be specific and correspond to single events. This
establishes a gap be- tween the previous training objective and real-world
applications, leading to the potential performance degradation of earlier
models during inference. In this study, we introduce the Multi-event Video-Text
Retrieval (MeVTR) task, addressing scenarios in which each video contains
multiple different events, as a niche scenario of the conventional Video-Text
Retrieval Task. We present a simple model, Me-Retriever, which incorporates key
event video representation and a new MeVTR loss for the MeVTR task.
Comprehensive experiments show that this straightforward framework outperforms
other models in the Video-to-Text and Text-to-Video tasks, effectively
establishing a robust baseline for the MeVTR task. We believe this work serves
as a strong foundation for future studies. Code is available at
this https URL

本研究介绍了多事件视频 - 文本检索（MeVTR）任务，旨在解决视频内容通常包含多个事件，而文本如用户查询或网页元数据倾向于特定和单一事件的实际情况。我们提出了一个简单的模型 Me-Retriever，它包含关键事件视频表示和新的 MeVTR 损失函数。通过全面的实验证明，这个简单的框架在视频到文本和文本到视频任务中胜过其他模型，为 MeVTR 任务建立了一个强大的基准。我们相信这项工作为未来的研究提供了坚实的基础。