论文标题
NVIDIA-UNIBZ提交Epic-Kitchens-100行动预期挑战2022
NVIDIA-UNIBZ Submission for EPIC-KITCHENS-100 Action Anticipation Challenge 2022
论文作者
论文摘要
在本报告中，我们描述了我们提交的Epic-Kitchen-100行动预期挑战的技术细节。我们的模型，高阶的复发时空变压器和带有边缘学习的消息通讯神经网络都是基于经常性的架构，仅观察2.5秒的推理上下文，以形成动作预期预测。通过平均通过我们建议的培训管道编译的一组模型的预测分数，我们在测试集上实现了强劲的表现，这是19.61％的总平均前5名召回率，在公共排行榜上被记录为第二名。
In this report, we describe the technical details of our submission for the EPIC-Kitchen-100 action anticipation challenge. Our modelings, the higher-order recurrent space-time transformer and the message-passing neural network with edge learning, are both recurrent-based architectures which observe only 2.5 seconds inference context to form the action anticipation prediction. By averaging the prediction scores from a set of models compiled with our proposed training pipeline, we achieved strong performance on the test set, which is 19.61% overall mean top-5 recall, recorded as second place on the public leaderboard.
