  Temporal action localization in untrimmed videos is an important but
difficult task. Difficulties are encountered in the application of existing
methods when modeling temporal structures of videos. In the present study, we
developed a novel method, referred to as Gemini Network, for effective modeling
of temporal structures and achieving high-performance temporal action
localization. The significant improvements afforded by the proposed method are
attributable to three major factors. First, the developed network utilizes two
subnets for effective modeling of temporal structures. Second, three parallel
feature extraction pipelines are used to prevent interference between the
extractions of different stage features. Third, the proposed method utilizes
auxiliary supervision, with the auxiliary classifier losses affording
additional constraints for improving the modeling capability of the network. As
a demonstration of its effectiveness, the Gemini Network was used to achieve
state-of-the-art temporal action localization performance on two challenging
datasets, namely, THUMOS14 and ActivityNet.
