  State-of-the-art text-video retrieval (TVR) methods typically utilize CLIP
and cosine similarity for efficient retrieval. Meanwhile, cross attention
methods, which employ a transformer decoder to compute attention between each
text query and all frames in a video, offer a more comprehensive interaction
between text and videos. However, these methods lack important fine-grained
spatial information as they directly compute attention between text and
video-level tokens. To address this issue, we propose CrossTVR, a two-stage
text-video retrieval architecture. In the first stage, we leverage existing TVR
methods with cosine similarity network for efficient text/video candidate
selection. In the second stage, we propose a novel decoupled video text cross
attention module to capture fine-grained multimodal information in spatial and
temporal dimensions. Additionally, we employ the frozen CLIP model strategy in
fine-grained retrieval, enabling scalability to larger pre-trained vision
models like ViT-G, resulting in improved retrieval performance. Experiments on
text video retrieval datasets demonstrate the effectiveness and scalability of
our proposed CrossTVR compared to state-of-the-art approaches.
