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Found 18 papers, 6 papers with code
A Survey on Knowledge Editing of Neural Networks
That is expensive, unreliable, and incompatible with the current trend of large self-supervised pre-training, making it necessary to find more efficient and effective methods for adapting neural network models to changing data.
Ultra-low-power Range Error Mitigation for Ultra-wideband Precise Localization
Precise and accurate localization in outdoor and indoor environments is a challenging problem that currently constitutes a significant limitation for several practical applications.
Back-to-Bones: Rediscovering the Role of Backbones in Domain Generalization
Domain Generalization (DG) studies the capability of a deep learning model to generalize to out-of-training distributions.
A Deep Learning Driven Algorithmic Pipeline for Autonomous Navigation in Row-Based Crops
Expensive sensors and inefficient algorithmic pipelines significantly affect the overall cost of autonomous machines.
Action Transformer: A Self-Attention Model for Short-Time Pose-Based Human Action Recognition
Deep neural networks based purely on attention have been successful across several domains, relying on minimal architectural priors from the designer.
Deep Semantic Segmentation at the Edge for Autonomous Navigation in Vineyard Rows
Precision agriculture is a fast-growing field that aims at introducing affordable and effective automation into agricultural processes.
Domain-Adversarial Training of Self-Attention Based Networks for Land Cover Classification using Multi-temporal Sentinel-2 Satellite Imagery
The increasing availability of large-scale remote sensing labeled data has prompted researchers to develop increasingly precise and accurate data-driven models for land cover and crop classification (LC&CC).
Efficient-CapsNet: Capsule Network with Self-Attention Routing
Deep convolutional neural networks, assisted by architectural design strategies, make extensive use of data augmentation techniques and layers with a high number of feature maps to embed object transformations.
Ranked #2 on
Image Classification
on MNIST
Robust Ultra-wideband Range Error Mitigation with Deep Learning at the Edge
Ultra-wideband (UWB) is the state-of-the-art and most popular technology for wireless localization.
Indoor Point-to-Point Navigation with Deep Reinforcement Learning and Ultra-wideband
Indoor autonomous navigation requires a precise and accurate localization system able to guide robots through cluttered, unstructured and dynamic environments.
DeepWay: a Deep Learning Waypoint Estimator for Global Path Generation
Agriculture 3. 0 and 4. 0 have gradually introduced service robotics and automation into several agricultural processes, mostly improving crops quality and seasonal yield.
DeepWay: A Deep Learning Estimator for Unmanned Ground Vehicle Global Path Planning
Agriculture 3. 0 and 4. 0 have gradually introduced service robotics and automation into several agricultural processes, mostly improving crops quality and seasonal yield.
A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge
The vital statistics of the last century highlight a sharp increment of the average age of the world population with a consequent growth of the number of older people.
Robotics
Multi-image Super Resolution of Remotely Sensed Images using Residual Feature Attention Deep Neural Networks
Convolutional Neural Networks (CNNs) have been consistently proved state-of-the-art results in image Super-Resolution (SR), representing an exceptional opportunity for the remote sensing field to extract further information and knowledge from captured data.
Ranked #1 on
Image Super-Resolution
on EPFL NIR-VIS
Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy
Concurrently, a second back-up algorithm, based on representations learning and resilient to illumination variations, can take control of the machine in case of a momentaneous failure of the first block.
UAV and Machine Learning Based Refinement of a Satellite-Driven Vegetation Index for Precision Agriculture
To achieve this objective, a reliable and updated description of the local status of crops is required.
Real-Time Apple Detection System Using Embedded Systems With Hardware Accelerators: An Edge AI Application
Real-time apple detection in orchards is one of the most effective ways of estimating apple yields, which helps in managing apple supplies more effectively.
Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)
The increasing spatial and temporal resolution of globally available satellite images, such as provided by Sentinel-2, creates new possibilities for researchers to use freely available multi-spectral optical images, with decametric spatial resolution and more frequent revisits for remote sensing applications such as land cover and crop classification (LC&CC), agricultural monitoring and management, environment monitoring.
