INTEGRATED MODALITIES AND MULTI-LEVEL GRANULARITY: TOWARDS A UNIFIED VIDEO-TEXT RETRIEVAL FRAMEWORK

Cross-modal retrieval between videos and texts has attracted growing attentions due to the rapid emergence of videos on the web. Recent researches handle different issues of this task such as exploiting multi-modal video cues, hierarchical reasoning, and learning pre-trained models. The implementations of these approaches vary a lot, which brings difficulty for the further research. Therefore, in this paper, we provide a unified video-text retrieval framework that has following features: 1) a modular design for easy modification of different structures of deep learning models; 2) training and test pipelines of the state-of-the-art (SOTA) models that leverage hierarchy cues and interactions between different levels of granularity and different video modalities; 3) support for various benchmark datasets; 4) demo exhibitions and well tested and documented. We hope our unified framework useful and efficient for the further research.