Baidu-UTS Submission to the EPIC-Kitchens Action Recognition Challenge 2019
This work addresses action recognition in kitchen videos for computer vision applications, but it is incremental as it builds on existing methods with a novel module.
The paper tackled action recognition in videos from the EPIC-Kitchens dataset, which involves challenges like small objects and motion blur, by using object detection features to guide 3D CNNs, resulting in improved noun prediction accuracy and outperforming other methods on test sets.
In this report, we present the Baidu-UTS submission to the EPIC-Kitchens Action Recognition Challenge in CVPR 2019. This is the winning solution to this challenge. In this task, the goal is to predict verbs, nouns, and actions from the vocabulary for each video segment. The EPIC-Kitchens dataset contains various small objects, intense motion blur, and occlusions. It is challenging to locate and recognize the object that an actor interacts with. To address these problems, we utilize object detection features to guide the training of 3D Convolutional Neural Networks (CNN), which can significantly improve the accuracy of noun prediction. Specifically, we introduce a Gated Feature Aggregator module to learn from the clip feature and the object feature. This module can strengthen the interaction between the two kinds of activations and avoid gradient exploding. Experimental results demonstrate our approach outperforms other methods on both seen and unseen test set.