  This report presents our team's 'PCIE_LAM' solution for the Ego4D Looking At
Me Challenge at CVPR2024. The main goal of the challenge is to accurately
determine if a person in the scene is looking at the camera wearer, based on a
video where the faces of social partners have been localized. Our proposed
solution, InternLSTM, consists of an InternVL image encoder and a Bi-LSTM
network. The InternVL extracts spatial features, while the Bi-LSTM extracts
temporal features. However, this task is highly challenging due to the distance
between the person in the scene and the camera movement, which results in
significant blurring in the face image. To address the complexity of the task,
we implemented a Gaze Smoothing filter to eliminate noise or spikes from the
output. Our approach achieved the 1st position in the looking at me challenge
with 0.81 mAP and 0.93 accuracy rate. Code is available at
https://github.com/KanokphanL/Ego4D_LAM_InternLSTM
