24 papers with code • 7 benchmarks • 8 datasets
First-person vision is gaining interest as it offers a unique viewpoint on people's interaction with objects, their attention, and even intention.
What Would You Expect? Anticipating Egocentric Actions with Rolling-Unrolling LSTMs and Modality Attention
Our method is ranked first in the public leaderboard of the EPIC-Kitchens egocentric action anticipation challenge 2019.
The hallucination task is treated as an auxiliary task, which can be used with any other action related task in a multitask learning setting.
The experiments show that the proposed architecture is state-of-the-art in the domain of egocentric videos, achieving top performances in the 2019 EPIC-Kitchens egocentric action anticipation challenge.
In contrast to the widely studied problem of recognizing an action given a complete sequence, action anticipation aims to identify the action from only partially available videos.
Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video
Motivated by this, we adopt intentional hand movement as a future representation and propose a novel deep network that jointly models and predicts the egocentric hand motion, interaction hotspots and future action.
To this end, we propose a solution for the problem of pedestrian action anticipation at the point of crossing.