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Reproducibility Report: Contrastive Learning of Socially-aware Motion Representations

arXiv 2208.09284 ↗First posted 18 Aug 2022cs.CV cs.LGcited by 0

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Abstract (arXiv)

The following paper is a reproducibility report for "Social NCE: Contrastive Learning of Socially-aware Motion Representations" citeliu2020snce published in ICCV 2021 as part of the ML Reproducibility Challenge 2021. The original code was made available by the author footnotehrefhttps://github.com/vita-epfl/social-ncehttps://github.com/vita-epfl/social-nce. We attempted to verify the results claimed by the authors and reimplemented their code in PyTorch Lightning.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 10 references that are in the map. First posted on arXiv 18 Aug 2022.

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References · 6

  1. 2017Rainbow: Combining Improvements in Deep Reinforcement Learningcited by 786
  2. 2020Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Predictioncited by 325
  3. 2018Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learningcited by 175
  4. 2021Multimodal Trajectory Prediction Conditioned on Lane-Graph Traversalscited by 113
  5. 2020Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Datacited by 58
  6. 2020Social NCE: Contrastive Learning of Socially-aware Motion Representationscited by 58

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