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Found 10 papers, 6 papers with code
Synthesizing Traffic Datasets using Graph Neural Networks
This paper introduces a novel methodology for bridging this `sim-real' gap by creating photorealistic images from 2D traffic simulations and recorded junction footage.
Principles and Guidelines for Evaluating Social Robot Navigation Algorithms
A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation.
Multi-person 3D pose estimation from unlabelled data
Its numerous applications make multi-human 3D pose estimation a remarkably impactful area of research.
Dyna-DM: Dynamic Object-aware Self-supervised Monocular Depth Maps
Much of the recent work focuses on improving depth estimation by increasing architecture complexity.
Fruit Quality and Defect Image Classification with Conditional GAN Data Augmentation
Contemporary Artificial Intelligence technologies allow for the employment of Computer Vision to discern good crops from bad, providing a step in the pipeline of selecting healthy fruit from undesirable fruit, such as those which are mouldy or gangrenous.
A Graph Neural Network to Model Disruption in Human-Aware Robot Navigation
This paper leverages Graph Neural Networks to model robot disruption considering the movement of the humans and the robot so that the model built can be used by path planning algorithms.
Generation of Human-aware Navigation Maps using Graph Neural Networks
Minimising the discomfort caused by robots when navigating in social situations is crucial for them to be accepted.
Multi-camera Torso Pose Estimation using Graph Neural Networks
The proposal presented in this paper makes use of graph neural networks to merge the information acquired from multiple camera sources, achieving a mean absolute error below 125 mm for the location and 10 degrees for the orientation using low-resolution RGB images.
Graph Neural Networks for Human-aware Social Navigation
Autonomous navigation is a key skill for assistive and service robots.
A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction
This study suggests a new approach to EEG data classification by exploring the idea of using evolutionary computation to both select useful discriminative EEG features and optimise the topology of Artificial Neural Networks.
