Are All Combinations Equal? Combining Textual and Visual Features with Multiple Space Learning for Text-Based Video Retrieval
This work addresses cross-modal retrieval for video search applications, presenting an incremental improvement in feature combination methods.
The paper tackles the problem of text-to-video retrieval by investigating optimal combinations of textual and visual features into multiple joint spaces, achieving performance documented through experiments on three large-scale datasets (IACC.3, V3C1, and MSR-VTT).
In this paper we tackle the cross-modal video retrieval problem and, more specifically, we focus on text-to-video retrieval. We investigate how to optimally combine multiple diverse textual and visual features into feature pairs that lead to generating multiple joint feature spaces, which encode text-video pairs into comparable representations. To learn these representations our proposed network architecture is trained by following a multiple space learning procedure. Moreover, at the retrieval stage, we introduce additional softmax operations for revising the inferred query-video similarities. Extensive experiments in several setups based on three large-scale datasets (IACC.3, V3C1, and MSR-VTT) lead to conclusions on how to best combine text-visual features and document the performance of the proposed network. Source code is made publicly available at: https://github.com/bmezaris/TextToVideoRetrieval-TtimesV