Current video retrieval systems, especially those used in competitions, primarily focus on querying individual keyframes or images rather than encoding an entire clip or video segment. However, queries often describe an action or event over a series of frames, not a specific image. This results in insufficient information when analyzing a single frame, leading to less accurate query results. Moreover, extracting embeddings solely from images (keyframes) does not provide enough information for models to encode higher-level, more abstract insights inferred from the video. These models tend to only describe the objects present in the frame, lacking a deeper understanding. In this work, we propose a system that integrates the latest methodologies, introducing a novel pipeline that extracts multimodal data, and incorporate information from multiple frames within a video, enabling the model to abstract higher-level information that captures latent meanings, focusing on what can be inferred from the video clip, rather than just focusing on object detection in one single image.

翻译：当前的视频检索系统，尤其是用于竞赛的系统，主要侧重于查询单个关键帧或图像，而非对整个片段或视频段落进行编码。然而，查询通常描述的是跨一系列帧的动作或事件，而非特定图像。这导致在分析单帧时信息不足，从而降低查询结果的准确性。此外，仅从图像（关键帧）中提取嵌入信息不足以让模型编码从视频中推断出的更高级、更抽象的见解，这类模型往往仅描述帧中存在的物体，缺乏更深层次的理解。在本研究中，我们提出了一种集成最新方法的系统，引入了一种新颖的流程来提取多模态数据，并整合视频中多个帧的信息，使模型能够抽象出捕捉潜在含义的高级信息，专注于从视频片段中可推断的内容，而非仅关注单张图像中的物体检测。