  Human capability to anticipate near future from visual observations and
non-verbal cues is essential for developing intelligent systems that need to
interact with people. Several research areas, such as human-robot interaction
(HRI), assisted living or autonomous driving need to foresee future events to
avoid crashes or help people. Egocentric scenarios are classic examples where
action anticipation is applied due to their numerous applications. Such
challenging task demands to capture and model domain's hidden structure to
reduce prediction uncertainty. Since multiple actions may equally occur in the
future, we treat action anticipation as a multi-label problem with missing
labels extending the concept of label smoothing. This idea resembles the
knowledge distillation process since useful information is injected into the
model during training. We implement a multi-modal framework based on long
short-term memory (LSTM) networks to summarize past observations and make
predictions at different time steps. We perform extensive experiments on
EPIC-Kitchens and EGTEA Gaze+ datasets including more than 2500 and 100 action
classes, respectively. The experiments show that label smoothing systematically
improves performance of state-of-the-art models for action anticipation.

Computer Vision and Pattern Recognition;Machine Learning