  Content-based video retrieval aims to find videos from a large video database
that are similar to or even near-duplicate of a given query video. Video
representation and similarity search algorithms are crucial to any video
retrieval system. To derive effective video representation, most video
retrieval systems require a large amount of manually annotated data for
training, making it costly inefficient. In addition, most retrieval systems are
based on frame-level features for video similarity searching, making it
expensive both storage wise and search wise. We propose a novel video retrieval
system, termed SVRTN, that effectively addresses the above shortcomings. It
first applies self-supervised training to effectively learn video
representation from unlabeled data to avoid the expensive cost of manual
annotation. Then, it exploits transformer structure to aggregate frame-level
features into clip-level to reduce both storage space and search complexity. It
can learn the complementary and discriminative information from the
interactions among clip frames, as well as acquire the frame permutation and
missing invariant ability to support more flexible retrieval manners.
Comprehensive experiments on two challenging video retrieval datasets, namely
FIVR-200K and SVD, verify the effectiveness of our proposed SVRTN method, which
achieves the best performance of video retrieval on accuracy and efficiency.
