Integrating Multiple Models For Effective Video Retrieval and Multi-stage Search
Bao Tran Gia, Tuong Bui Cong Khanh, Khoa Tran, Kien Luu Trung, Thuyen Tran Doan, Khiem Le, Tien Do, Thanh Duc Ngo · 2023
Video is one of the most prevalent forms of data due to the widespread availability of recording devices. This makes video retrieval systems essential since they assist in locating a video segment within a dataset that most closely matches a given query. One of the difficulties of video querying is the processing of multimedia data (images, audio, and text). In addition, it is important to integrate temporal information, as inquiries frequently pertain to the depiction of events occurring within a specific time frame. Thus, this study introduces an innovative system capable of not only integrating various types of models but also effectively managing temporal searches through multi-stage processes. The efficacy of the systems was demonstrated at the AI Challenge 2023, which took place in Ho Chi Minh City, where our team got the best accuracy across all other contestants in the qualifying phase and received the top 1 position among the 60 participating teams.