Reviews: Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks

Summary: The paper proposed to improve the interaction modeling between pedestrians by using a graph attention network [22] for the trajectory prediction task and learn multimodal trajectory distributions by using Bicycle-GAN [23]. The experimental results showed the effectiveness of the proposed approach by achieving state-of-the-art performance on the public benchmarks. Also, they showed that the performance of the proposed approach is more robust to varying K than that of the baselines, indicating that the proposed approach was successful in addressing the high variance issue in the existing approaches to a certain extent. Strengths: -- The paper is clearly written so it was easy to follow. The reasoning behind the choice of [22] and [23] for the trajectory prediction task is also clearly presented in the paper.