  Self-Supervised Video Hashing (SSVH) models learn to generate short binary
representations for videos without ground-truth supervision, facilitating
large-scale video retrieval efficiency and attracting increasing research
attention. The success of SSVH lies in the understanding of video content and
the ability to capture the semantic relation among unlabeled videos. Typically,
state-of-the-art SSVH methods consider these two points in a two-stage training
pipeline, where they firstly train an auxiliary network by instance-wise
mask-and-predict tasks and secondly train a hashing model to preserve the
pseudo-neighborhood structure transferred from the auxiliary network. This
consecutive training strategy is inflexible and also unnecessary. In this
paper, we propose a simple yet effective one-stage SSVH method called ConMH,
which incorporates video semantic information and video similarity relationship
understanding in a single stage. To capture video semantic information for
better hashing learning, we adopt an encoder-decoder structure to reconstruct
the video from its temporal-masked frames. Particularly, we find that a higher
masking ratio helps video understanding. Besides, we fully exploit the
similarity relationship between videos by maximizing agreement between two
augmented views of a video, which contributes to more discriminative and robust
hash codes. Extensive experiments on three large-scale video datasets (i.e.,
FCVID, ActivityNet and YFCC) indicate that ConMH achieves state-of-the-art
results. Code is available at https://github.com/huangmozhi9527/ConMH.
