  Temporal action proposal generation is an essential and challenging task that
aims at localizing temporal intervals containing human actions in untrimmed
videos. Most of existing approaches are unable to follow the human cognitive
process of understanding the video context due to lack of attention mechanism
to express the concept of an action or an agent who performs the action or the
interaction between the agent and the environment. Based on the action
definition that a human, known as an agent, interacts with the environment and
performs an action that affects the environment, we propose a contextual
Agent-Environment Network. Our proposed contextual AEN involves (i) agent
pathway, operating at a local level to tell about which humans/agents are
acting and (ii) environment pathway operating at a global level to tell about
how the agents interact with the environment. Comprehensive evaluations on
20-action THUMOS-14 and 200-action ActivityNet-1.3 datasets with different
backbone networks, i.e C3D and SlowFast, show that our method robustly exhibits
outperformance against state-of-the-art methods regardless of the employed
backbone network.
