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Found 12 papers, 6 papers with code
TransGlow: Attention-augmented Transduction model based on Graph Neural Networks for Water Flow Forecasting
Since water systems are interconnected and the connectivity information between the stations is implicit, the proposed model leverages a graph learning module to extract a sparse graph adjacency matrix adaptively based on the data.
Efficient Deduplication and Leakage Detection in Large Scale Image Datasets with a focus on the CrowdAI Mapping Challenge Dataset
The CrowdAI Mapping Challenge Dataset is one of these datasets that has been used extensively in recent years to train deep neural networks.
End-to-End Multi-View Structure-from-Motion with Hypercorrelation Volumes
Image-based 3D reconstruction is one of the most important tasks in Computer Vision with many solutions proposed over the last few decades.
Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction
In recent years, graph neural networks (GNNs) combined with variants of recurrent neural networks (RNNs) have reached state-of-the-art performance in spatiotemporal forecasting tasks.
Unsupervised Structure-Consistent Image-to-Image Translation
The Swapping Autoencoder achieved state-of-the-art performance in deep image manipulation and image-to-image translation.
Motion Estimation for Large Displacements and Deformations
Variational optical flow techniques based on a coarse-to-fine scheme interpolate sparse matches and locally optimize an energy model conditioned on colour, gradient and smoothness, making them sensitive to noise in the sparse matches, deformations, and arbitrarily large displacements.
Adaptive Memory Management for Video Object Segmentation
Matching-based networks have achieved state-of-the-art performance for video object segmentation (VOS) tasks by storing every-k frames in an external memory bank for future inference.
Multi-view Gradient Consistency for SVBRDF Estimation of Complex Scenes under Natural Illumination
In contrast to previous methods, our process is not limited to scenes viewed under controlled lighting conditions but can handle complex indoor and outdoor scenes viewed under arbitrary illumination conditions.
Predicting Surface Reflectance Properties of Outdoor Scenes Under Unknown Natural Illumination
Estimating and modelling the appearance of an object under outdoor illumination conditions is a complex process.
Semantic Segmentation from Remote Sensor Data and the Exploitation of Latent Learning for Classification of Auxiliary Tasks
In this paper we address three different aspects of semantic segmentation from remote sensor data using deep neural networks.
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Multi-label Pixelwise Classification for Reconstruction of Large-scale Urban Areas
In fact, for single-label object classification [i. e. only one object present in the image] the state-of-the-art techniques employ deep neural networks and are reporting very close to human-like performance.
3DUNDERWORLD-SLS: An Open-Source Structured-Light Scanning System for Rapid Geometry Acquisition
Recently, there has been an increase in the demand of virtual 3D objects representing real-life objects.
