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Found 14 papers, 6 papers with code
Morphological Classification of Radio Galaxies using Semi-Supervised Group Equivariant CNNs
Out of the estimated few trillion galaxies, only around a million have been detected through radio frequencies, and only a tiny fraction, approximately a thousand, have been manually classified.
Automatic Detection of Natural Disaster Effect on Paddy Field from Satellite Images using Deep Learning Techniques
The areas equal to and above the 0. 33 threshold are marked as crop loss areas as significant changes are observed.
Variational Stacked Local Attention Networks for Diverse Video Captioning
However, growing model complexity for visual data encourages more explicit feature interaction for fine-grained information, which is currently absent in the video captioning domain.
Deep Dive into Semi-Supervised ELBO for Improving Classification Performance
Experiments on a diverse datasets verify that our method can be used to improve the classification performance of existing VAE based semi-supervised models.
A Novel Disaster Image Dataset and Characteristics Analysis using Attention Model
In this research, we have carefully accumulated a relatively challenging dataset that contains images collected from various sources for three different disasters: fire, water and land.
Attention Toward Neighbors: A Context Aware Framework for High Resolution Image Segmentation
High-resolution image segmentation remains challenging and error-prone due to the enormous size of intermediate feature maps.
Node Embedding using Mutual Information and Self-Supervision based Bi-level Aggregation
Graph Neural Networks (GNNs) learn low dimensional representations of nodes by aggregating information from their neighborhood in graphs.
Structure-Aware Hierarchical Graph Pooling using Information Bottleneck
Graph pooling is an essential ingredient of Graph Neural Networks (GNNs) in graph classification and regression tasks.
Unified Spatio-Temporal Modeling for Traffic Forecasting using Graph Neural Network
However, most state-of-the-art approaches have designed spatial-only (e. g. Graph Neural Networks) and temporal-only (e. g. Recurrent Neural Networks) modules to separately extract spatial and temporal features.
SST-GNN: Simplified Spatio-temporal Traffic forecasting model using Graph Neural Network
Most of the recent works employed graph neural networks(GNN) with multiple layers to capture the spatial dependency.
Hierarchical Self Attention Based Autoencoder for Open-Set Human Activity Recognition
Wearable sensor based human activity recognition is a challenging problem due to difficulty in modeling spatial and temporal dependencies of sensor signals.
Deep-learning coupled with novel classification method to classify the urban environment of the developing world
Image encompassing 70% of the urban space was used for training and the remaining 30% was used for testing and validation.
Human Activity Recognition from Wearable Sensor Data Using Self-Attention
In this regard, the existing recurrent or convolutional or their hybrid models for activity recognition struggle to capture spatio-temporal context from the feature space of sensor reading sequence.
Can a simple approach identify complex nurse care activity?
For the last two decades, more and more complex methods have been developed to identify human activities using various types of sensors, e. g., data from motion capture, accelerometer, and gyroscopes sensors.
