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Found 9 papers, 6 papers with code
Exploring 3D Human Pose Estimation and Forecasting from the Robot's Perspective: The HARPER Dataset
The scenario underlying HARPER includes 15 actions, of which 10 involve physical contact between the robot and users.
A Machine Learning-oriented Survey on Tiny Machine Learning
The emergence of Tiny Machine Learning (TinyML) has positively revolutionized the field of Artificial Intelligence by promoting the joint design of resource-constrained IoT hardware devices and their learning-based software architectures.
Language-enhanced RNR-Map: Querying Renderable Neural Radiance Field maps with natural language
We present Le-RNR-Map, a Language-enhanced Renderable Neural Radiance map for Visual Navigation with natural language query prompts.
Markerless human pose estimation for biomedical applications: a survey
Markerless Human Pose Estimation (HPE) proved its potential to support decision making and assessment in many fields of application.
OO-dMVMT: A Deep Multi-view Multi-task Classification Framework for Real-time 3D Hand Gesture Classification and Segmentation
Continuous mid-air hand gesture recognition based on captured hand pose streams is fundamental for human-computer interaction, particularly in AR / VR.
Split-Et-Impera: A Framework for the Design of Distributed Deep Learning Applications
Many recent pattern recognition applications rely on complex distributed architectures in which sensing and computational nodes interact together through a communication network.
A Masked Face Classification Benchmark on Low-Resolution Surveillance Images
Results show that the richness of SF-MASK (real + synthetic images) leads all of the tested classifiers to perform better than exploiting comparative face mask datasets, on a fixed 1077 images testing set.
I-SPLIT: Deep Network Interpretability for Split Computing
A neuron is important if its gradient with respect to the correct class decision is high.
Pose Forecasting in Industrial Human-Robot Collaboration
Pushing back the frontiers of collaborative robots in industrial environments, we propose a new Separable-Sparse Graph Convolutional Network (SeS-GCN) for pose forecasting.
