Free-form Multi-Modal Multimedia Retrieval (4MR)

Rahel Arnold, Loris Sauter, Heiko Schuldt
In Proceedings
Appears in
Proceedings of the 29th International Conference on Multimedia Modeling (MMM 2023)
Bergen, Norway

Due to the ever increasing amount of multimedia data, efficient means for multimedia management and retrieval are required. Especially with the rise of deep-learning-based analytics methods, the semantic gap has shrunk considerably, but a human in the loop is still considered mandatory. One of the driving factors of video search is that humans tend to refine their queries after reviewing the results. Hence, the entire process is highly interactive. A natural approach to interactive video search is using textual descriptions of the content of the expected result, enabled by deep learning-based joint visual text co-embedding. In this paper, we present the Multi-Modal Multimedia Retrieval (4MR) system, a novel system inspired by vitrivr, that empowers users with almost entirely free-form query formulation methods. The top-ranked teams of the last few iterations of the Video Browser Showdown have shown that CLIP provides an ideal feature extraction method. Therefore, while 4MR is capable of image and text retrieval as well, for VBS video retrieval is based primarily based on CLIP.


VBS 2023

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