  Adapting large-scale image-text pre-training models, e.g., CLIP, to the video
domain represents the current state-of-the-art for text-video retrieval. The
primary approaches involve transferring text-video pairs to a common embedding
space and leveraging cross-modal interactions on specific entities for semantic
alignment. Though effective, these paradigms entail prohibitive computational
costs, leading to inefficient retrieval. To address this, we propose a simple
yet effective method, Global-Local Semantic Consistent Learning (GLSCL), which
capitalizes on latent shared semantics across modalities for text-video
retrieval. Specifically, we introduce a parameter-free global interaction
module to explore coarse-grained alignment. Then, we devise a shared local
interaction module that employs several learnable queries to capture latent
semantic concepts for learning fine-grained alignment. Furthermore, an
Inter-Consistency Loss (ICL) is devised to accomplish the concept alignment
between the visual query and corresponding textual query, and an
Intra-Diversity Loss (IDL) is developed to repulse the distribution within
visual (textual) queries to generate more discriminative concepts. Extensive
experiments on five widely used benchmarks (i.e., MSR-VTT, MSVD, DiDeMo, LSMDC,
and ActivityNet) substantiate the superior effectiveness and efficiency of the
proposed method. Remarkably, our method achieves comparable performance with
SOTA as well as being nearly 220 times faster in terms of computational cost.
Code is available at: https://github.com/zchoi/GLSCL.
