  With the recent boom of video-based social platforms (e.g., YouTube and
TikTok), video retrieval using sentence queries has become an important demand
and attracts increasing research attention. Despite the decent performance,
existing text-video retrieval models in vision and language communities are
impractical for large-scale Web search because they adopt brute-force search
based on high-dimensional embeddings. To improve efficiency, Web search engines
widely apply vector compression libraries (e.g., FAISS) to post-process the
learned embeddings. Unfortunately, separate compression from feature encoding
degrades the robustness of representations and incurs performance decay. To
pursue a better balance between performance and efficiency, we propose the
first quantized representation learning method for cross-view video retrieval,
namely Hybrid Contrastive Quantization (HCQ). Specifically, HCQ learns both
coarse-grained and fine-grained quantizations with transformers, which provide
complementary understandings for texts and videos and preserve comprehensive
semantic information. By performing Asymmetric-Quantized Contrastive Learning
(AQ-CL) across views, HCQ aligns texts and videos at coarse-grained and
multiple fine-grained levels. This hybrid-grained learning strategy serves as
strong supervision on the cross-view video quantization model, where
contrastive learning at different levels can be mutually promoted. Extensive
experiments on three Web video benchmark datasets demonstrate that HCQ achieves
competitive performance with state-of-the-art non-compressed retrieval methods
while showing high efficiency in storage and computation. Code and
configurations are available at https://github.com/gimpong/WWW22-HCQ.
