BlazeSearch: A multimomal semantic search engine for retrieving in-video information for AI Challenge HCMC 2023
Ngo Duc Hoang Son, Anh Bui Vuong Tam, Phuoc Phan Hoang, Giang Tran Thi Cam, Thịnh Nguyễn Hưng, Nguyen Huu Quyen, Phan The Duy, Van-Hau Pham · 2023
In the world today, exploring information has become a critical part of modern life. As a result, search engines have shown their ability to enhance the knowledge-seeking process. However, these search engines still focus on searching for websites or images. The capacity to find information in videos is extremely needed to experiment and study more in order to improve the power of search engines. In this study, we investigate the potentiality of in-video information search engines by introducing BlazeSearch, a multimodal search engine designed to search frames of video with simple input text. By leveraging the OpenCLIP model, which is superior for the image-text retrieval task, our search engine can be guaranteed reliability and accuracy. Furthermore, we optimize the searching speed and provide an easy-to-use, fully functional user interface for BlazeSearch, which can help users have a pleasant experience.