Year: 2025
Type: conference-paper
Abstract: Currently, video is the most consumed content on the internet, with an increasing amount of information and knowledge shared as videos. This has led to a significant rise in video search requests. On Bilibili, one of China's largest knowledge-based video platforms, over half of daily users search for videos. Recent advancements in multimodal embeddings and text-to-video retrieval have shown promise, yet large-scale systems require search results within seconds. The high computational costs of image-based models have limited their scalability in video retrieval, with text-based searches remaining dominant and many video contents under-indexed.
Authors: Qiang Zhang, Fengshun Xiao, Tianjiao Li, Li Lin, Hanyin Fang, Huyang Sun, Ruoyu Liu, Xiaoyan Zhu, Jiayin Wang
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4410636351