Intention-Conditioned Long-Term Human Egocentric Action Forecasting

Understand

To anticipate how a human would act in the future, it is essential to understand the human intention since it guides the human towards a certain goal.

• In this paper, we propose a hierarchical architecture which assumes a sequence of human action (low-level) can be driven from the human intention (high-level).
• Based on this, we deal with Long-Term Action Anticipation task in egocentric videos.
• Our framework first extracts two level of human information over the N observed videos human actions through a Hierarchical Multi-task MLP Mixer (H3M).