𝗗𝗮𝘆-𝟮𝟯𝟱 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗠𝗚-𝗚𝗔𝗡: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction by Technical University of Munich Follow me for a similar post: 🇮🇳 Ashish Patel Interesting Facts : 🔸 This paper is published #ICCV2021 . ------------------------------------------------------------------- 𝗔𝗺𝗮𝘇𝗶𝗻𝗴 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 : https://lnkd.in/erQsB3Mc Code: https://lnkd.in/e-wkiNd4 ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸Pedestrian trajectory prediction is challenging due to its uncertain and multimodal nature. 🔸While generative adversarial networks can learn a distribution over future trajectories, they tend to predict out-of-distribution samples when the distribution of future trajectories is a mixture of multiple, possibly disconnected modes. 🔸To address this issue, we propose a multi-generator model for pedestrian trajectory prediction. 🔸Each generator specializes in learning a distribution over trajectories routing towards one of the primary modes in the scene, while a second network learns a categorical distribution over these generators, conditioned on the dynamics and scene input. 🔸This architecture allows us to effectively sample from specialized generators and to significantly reduce the out-of-distribution samples compared to single generator methods.. #computervision #artificialintelligence