BiC-Net: Learning Efficient Spatio-Temporal Relation for Text-Video Retrieval. (arXiv:2110.15609v3 [cs.CV] UPDATED)

The task of text-video retrieval aims to understand the correspondence
between language and vision, has gained increasing attention in recent years.
Previous studies either adopt off-the-shelf 2D/3D-CNN and then use average/max
pooling to directly capture spatial features with aggregated temporal
information as global video embeddings, or introduce graph-based models and
expert knowledge to learn local spatial-temporal relations. However, the
existing methods have two limitations: 1) The global video representations
learn video temporal information in a simple average/max pooling manner and …

arxiv bic cv learning retrieval temporal text video