In this report, we describe the technical details of our approach for the
Ego4D Long-Term Action Anticipation Challenge 2023. The aim of this task is to
predict a sequence of future actions that will take place at an arbitrary time
or later, given an input video. To accomplish this task, we introduce three
improvements to the baseline model, which consists of an encoder that generates
clip-level features from the video, an aggregator that integrates multiple
clip-level features, and a decoder that outputs Z future actions. 1) Model
ensemble of SlowFast and SlowFast-CLIP; 2) Label smoothing to relax order
constraints for future actions; 3) Constraining the prediction of the action
class (verb, noun) based on word co-occurrence. Our method outperformed the
baseline performance and recorded as second place solution on the public
leaderboard.

本报告描述了我们在 Ego4D 長期動作預測挑战赛 2023 中的技术细节，我们引入了三个改进，包括 SlowFast 和 SlowFast-CLIP 模型的集合，放松未来动作的顺序限制，以及基于词共现的操作类（动词、名词）的预测，优化了基线性能，取得了公共排行榜的第二名。