In this technical report, we describe the Guided-Attention mechanism based solution for the short-term anticipation (STA) challenge for the EGO4D challenge. It combines the object detections, and the spatiotemporal features extracted from video clips, enhancing the motion and contextual information, and further decoding the object-centric and motion-centric information to address the problem of STA in egocentric videos. For the challenge, we build our model on top of StillFast with Guided Attention applied on fast network. Our model obtains better performance on the validation set and also achieves state-of-the-art (SOTA) results on the challenge test set for EGO4D Short-Term Object Interaction Anticipation Challenge.

本技术报告提出了一种基于引导注意力机制的解决方案来解决EGO4D短期预测挑战，该解决方案结合了物体检测和从视频剪辑中提取的时空特征，增强了运动和上下文信息，并进一步解码物体中心和运动中心的信息以解决EGO4D短期对象交互预测挑战。我们在快速网络上应用引导关注力，构建我们的模型，该模型在验证集上获得了更好的性能，并在EGO4D短期对象交互预测挑战的测试集上取得了最佳成绩。

EGO4D STA挑战赛下一个活动对象的引导关注