The rapid growth of video on the internet has made searching for video content using natural language queries a significant challenge. Human generated queries for video datasets `in the wild' vary a lot in terms of degree of specificity, with some queries describing `specific details' such as the names of famous identities, content from speech, or text available on the screen. Our goal is to condense the multi-modal, extremely high dimensional information from videos into a single, compact video representation for the task of video retrieval using free-form text queries, where the degree of specificity is open-ended. For this we exploit existing knowledge in the form of pretrained semantic embeddings which include `general' features such as motion, appearance, and scene features from visual content, and more `specific' cues from ASR and OCR which may not always be available, but allow for more fine-grained disambiguation when present. We propose a collaborative experts model to aggregate information effectively from these different pretrained experts. The effectiveness of our approach is demonstrated empirically, setting new state-of-the-art performances on five retrieval benchmarks: MSR-VTT, LSMDC, MSVD, DiDeMo, and ActivityNet, while simultaneously reducing the number of parameters used by prior work. Code and data can be found at www.robots.ox.ac.uk/~vgg/research/collaborative-experts/.

该论文旨在将来自不同模态的视频信息压缩为单一、紧凑的表示形式，以用于自由格式文本查询的视频检索任务。通过运用预训练的语义嵌入、自动语音识别和光学字符识别等方法，采用协作专家模型来聚合信息，具有良好的检索性能。

利用现有资源：使用协同信息源提取视频表示