Video Retrieval Model

Video retrieval models aim to efficiently and accurately retrieve videos from large databases based on text queries or vice-versa. Current research focuses on improving the effectiveness of contrastive learning methods by addressing issues like imbalanced negative samples and developing more sophisticated similarity measures, often incorporating hierarchical learning or adaptive margins. These advancements leverage pre-trained models like CLIP and transformers, with ongoing efforts to optimize computational efficiency through techniques such as token selection and clustering. The resulting improvements in video retrieval have significant implications for applications ranging from web-scale video search to personalized media management.