Temporal language grounding in videos aims to localize the temporal span relevant to the given query sentence.
Accurate temporal action proposals play an important role in detecting actions from untrimmed videos.
A collection of approaches based on graph convolutional networks have proven success in skeleton-based action recognition by exploring neighborhood information and dense dependencies between intra-frame joints.
Ranked #9 on Skeleton Based Action Recognition on NTU RGB+D
In this technical report, we describe our solution to temporal action proposal (task 1) in ActivityNet Challenge 2019.