BMN: Boundary-Matching Network for Temporal Action Proposal Generation
This addresses the challenge of efficiently locating action regions in videos for applications like video analysis, though it appears incremental as it builds on existing bottom-up methods.
The paper tackles the problem of generating temporal action proposals in videos by introducing the Boundary-Matching Network (BMN), which simultaneously produces proposals with precise boundaries and reliable confidence scores, achieving significant performance improvements on THUMOS-14 and ActivityNet-1.3 datasets.
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.