Trajectory Prediction with Graph-based Dual-scale Context Fusion

Last update: Jan 04, 2023

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Deep Learning DSP

Overview

DSP: Trajectory Prediction with Graph-based Dual-scale Context Fusion

Introduction

This is the project page of the paper

Lu Zhang, Peiliang Li, Jing Chen and Shaojie Shen, "Trajectory Prediction with Graph-based Dual-scale Context Fusion", 2021,

which is submitted to the IEEE Robotics and Automation Letters (RA-L).

Notice: The code will be released after the publishing of this paper.

Preprint: Link

Video:

Demo

Color scheme: green - predicted trajectories; red - observation & GT trajectories; orange - other agents.

Owner

HKUST Aerial Robotics Group

GitHub Repository

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Trajectory Prediction with Graph-based Dual-scale Context Fusion

Related tags

Overview

DSP: Trajectory Prediction with Graph-based Dual-scale Context Fusion

Introduction

Demo

Owner

HKUST Aerial Robotics Group

Code for the paper: Hierarchical Reinforcement Learning With Timed Subgoals, published at NeurIPS 2021

Code for CMaskTrack R-CNN (proposed in Occluded Video Instance Segmentation)

Extracting knowledge graphs from language models as a diagnostic benchmark of model performance.

Data Augmentation Using Keras and Python

cl;asification problem using classification models in supervised learning

Official Pytorch Implementation of: "ImageNet-21K Pretraining for the Masses"(2021) paper

Aircraft design optimization made fast through modern automatic differentiation

Convert scikit-learn models to PyTorch modules

🏆 The 1st Place Submission to AICity Challenge 2021 Natural Language-Based Vehicle Retrieval Track (Alibaba-UTS submission)

Offical code for the paper: "Growing 3D Artefacts and Functional Machines with Neural Cellular Automata" https://arxiv.org/abs/2103.08737

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