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Found 8 papers, 5 papers with code
Motion Style Transfer: Modular Low-Rank Adaptation for Deep Motion Forecasting
To this end, we introduce two components that exploit our prior knowledge of motion style shifts: (i) a low-rank motion style adapter that projects and adjusts the style features at a low-dimensional bottleneck; and (ii) a modular adapter strategy that disentangles the features of scene context and motion history to facilitate a fine-grained choice of adaptation layers.
Safety-compliant Generative Adversarial Networks for Human Trajectory Forecasting
Human trajectory forecasting in crowds presents the challenges of modelling social interactions and outputting collision-free multimodal distribution.
TF-GNN: Graph Neural Networks in TensorFlow
TensorFlow-GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow.
TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive?
In this work, we first provide an in-depth look at its limitations and show that TTT can possibly deteriorate, instead of improving, the test-time performance in the presence of severe distribution shifts.
DriverGym: Democratising Reinforcement Learning for Autonomous Driving
Despite promising progress in reinforcement learning (RL), developing algorithms for autonomous driving (AD) remains challenging: one of the critical issues being the absence of an open-source platform capable of training and effectively validating the RL policies on real-world data.
Interpretable Social Anchors for Human Trajectory Forecasting in Crowds
Human trajectory forecasting in crowds, at its core, is a sequence prediction problem with specific challenges of capturing inter-sequence dependencies (social interactions) and consequently predicting socially-compliant multimodal distributions.
Human Trajectory Forecasting in Crowds: A Deep Learning Perspective
In this work, we present an in-depth analysis of existing deep learning-based methods for modelling social interactions.
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Trajectory Prediction
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Collaborative Sampling in Generative Adversarial Networks
The standard practice in Generative Adversarial Networks (GANs) discards the discriminator during sampling.
