  Video Moment Retrieval (VMR) aims at retrieving the most relevant events from
an untrimmed video with natural language queries. Existing VMR methods suffer
from two defects: (1) massive expensive temporal annotations are required to
obtain satisfying performance; (2) complicated cross-modal interaction modules
are deployed, which lead to high computational cost and low efficiency for the
retrieval process. To address these issues, we propose a novel method termed
Cheaper and Faster Moment Retrieval (CFMR), which well balances the retrieval
accuracy, efficiency, and annotation cost for VMR. Specifically, our proposed
CFMR method learns from point-level supervision where each annotation is a
single frame randomly located within the target moment. It is 6 times cheaper
than the conventional annotations of event boundaries. Furthermore, we also
design a concept-based multimodal alignment mechanism to bypass the usage of
cross-modal interaction modules during the inference process, remarkably
improving retrieval efficiency. The experimental results on three widely used
VMR benchmarks demonstrate the proposed CFMR method establishes new
state-of-the-art with point-level supervision. Moreover, it significantly
accelerates the retrieval speed with more than 100 times FLOPs compared to
existing approaches with point-level supervision.
