  Video activity anticipation aims to predict what will happen in the future,
embracing a broad application prospect ranging from robot vision and autonomous
driving. Despite the recent progress, the data uncertainty issue, reflected as
the content evolution process and dynamic correlation in event labels, has been
somehow ignored. This reduces the model generalization ability and deep
understanding on video content, leading to serious error accumulation and
degraded performance. In this paper, we address the uncertainty learning
problem and propose an uncertainty-boosted robust video activity anticipation
framework, which generates uncertainty values to indicate the credibility of
the anticipation results. The uncertainty value is used to derive a temperature
parameter in the softmax function to modulate the predicted target activity
distribution. To guarantee the distribution adjustment, we construct a
reasonable target activity label representation by incorporating the activity
evolution from the temporal class correlation and the semantic relationship.
Moreover, we quantify the uncertainty into relative values by comparing the
uncertainty among sample pairs and their temporal-lengths. This relative
strategy provides a more accessible way in uncertainty modeling than
quantifying the absolute uncertainty values on the whole dataset. Experiments
on multiple backbones and benchmarks show our framework achieves promising
performance and better robustness/interpretability. Source codes are available
at https://github.com/qzhb/UbRV2A.
