Social-Mamba introduces a Cycle Mamba block and social triplet factorization to achieve state-of-the-art trajectory forecasting accuracy with linear-time social interaction modeling on five benchmarks.
arXiv preprint arXiv:2312.16168 (2023) 2, 10, 11, 14
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A two-mode pedestrian trajectory predictor, separating crossing from non-crossing futures, beats prior models on PIE and JAAD and can be plugged into existing predictors like BiTrap and SGNet.
Three-step hierarchical Transformer separates temporal, multimodal, and social reasoning using GRU summaries and time-agent attention, achieving SOTA on JRDB and Urban datasets.
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Social-Mamba: Socially-Aware Trajectory Forecasting with State-Space Models
Social-Mamba introduces a Cycle Mamba block and social triplet factorization to achieve state-of-the-art trajectory forecasting accuracy with linear-time social interaction modeling on five benchmarks.
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Where Will They Go? Modelling Multimodal Pedestrian Manoeuvres from Ego-centric Videos
A two-mode pedestrian trajectory predictor, separating crossing from non-crossing futures, beats prior models on PIE and JAAD and can be plugged into existing predictors like BiTrap and SGNet.
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Three-Step Hierarchical Transformer for Multi-Pedestrian Trajectory Prediction
Three-step hierarchical Transformer separates temporal, multimodal, and social reasoning using GRU summaries and time-agent attention, achieving SOTA on JRDB and Urban datasets.