  Temporal action proposal generation is an challenging and promising task
which aims to locate temporal regions in real-world videos where action or
event may occur. Current bottom-up proposal generation methods can generate
proposals with precise boundary, but cannot efficiently generate adequately
reliable confidence scores for retrieving proposals. To address these
difficulties, we introduce the Boundary-Matching (BM) mechanism to evaluate
confidence scores of densely distributed proposals, which denote a proposal as
a matching pair of starting and ending boundaries and combine all densely
distributed BM pairs into the BM confidence map. Based on BM mechanism, we
propose an effective, efficient and end-to-end proposal generation method,
named Boundary-Matching Network (BMN), which generates proposals with precise
temporal boundaries as well as reliable confidence scores simultaneously. The
two-branches of BMN are jointly trained in an unified framework. We conduct
experiments on two challenging datasets: THUMOS-14 and ActivityNet-1.3, where
BMN shows significant performance improvement with remarkable efficiency and
generalizability. Further, combining with existing action classifier, BMN can
achieve state-of-the-art temporal action detection performance.
