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 this https URL

本论文提出了一种不确定性增强的强鲁棒性视频活动预测框架，通过生成不确定性值来表示预测结果的可信度，并利用该值来调整预测目标活动的分布。同时，通过将活动的演化和语义关系结合，构建了合理的目标活动标签表示。此外，通过比较样本对和它们的时间长度之间的不确定性，将不确定性量化为相对值，提供了一种更可行的不确定性建模方法。实验结果表明，提出的框架在多个基准数据集上取得了有希望的性能和更好的鲁棒性 / 可解释性。