  Detecting activities in untrimmed videos is an important but challenging
task. The performance of existing methods remains unsatisfactory, e.g., they
often meet difficulties in locating the beginning and end of a long complex
action. In this paper, we propose a generic framework that can accurately
detect a wide variety of activities from untrimmed videos. Our first
contribution is a novel proposal scheme that can efficiently generate
candidates with accurate temporal boundaries. The other contribution is a
cascaded classification pipeline that explicitly distinguishes between
relevance and completeness of a candidate instance. On two challenging temporal
activity detection datasets, THUMOS14 and ActivityNet, the proposed framework
significantly outperforms the existing state-of-the-art methods, demonstrating
superior accuracy and strong adaptivity in handling activities with various
temporal structures.
