  Temporal action proposal generation (TAPG) is a fundamental and challenging
task in video understanding, especially in temporal action detection. Most
previous works focus on capturing the local temporal context and can well
locate simple action instances with clean frames and clear boundaries. However,
they generally fail in complicated scenarios where interested actions involve
irrelevant frames and background clutters, and the local temporal context
becomes less effective. To deal with these problems, we present an augmented
transformer with adaptive graph network (ATAG) to exploit both long-range and
local temporal contexts for TAPG. Specifically, we enhance the vanilla
transformer by equipping a snippet actionness loss and a front block, dubbed
augmented transformer, and it improves the abilities of capturing long-range
dependencies and learning robust feature for noisy action instances.Moreover,
an adaptive graph convolutional network (GCN) is proposed to build local
temporal context by mining the position information and difference between
adjacent features. The features from the two modules carry rich semantic
information of the video, and are fused for effective sequential proposal
generation. Extensive experiments are conducted on two challenging datasets,
THUMOS14 and ActivityNet1.3, and the results demonstrate that our method
outperforms state-of-the-art TAPG methods. Our code will be released soon.
