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Found 11 papers, 6 papers with code
HAMLET: Graph Transformer Neural Operator for Partial Differential Equations
We present a novel graph transformer framework, HAMLET, designed to address the challenges in solving partial differential equations (PDEs) using neural networks.
TrafficMOT: A Challenging Dataset for Multi-Object Tracking in Complex Traffic Scenarios
Multi-object tracking in traffic videos is a crucial research area, offering immense potential for enhancing traffic monitoring accuracy and promoting road safety measures through the utilisation of advanced machine learning algorithms.
CDiffMR: Can We Replace the Gaussian Noise with K-Space Undersampling for Fast MRI?
Different from conventional diffusion models, the degradation operation of our CDiffMR is based on \textit{k}-space undersampling instead of adding Gaussian noise, and the restoration network is trained to harness a de-aliaseing function.
ViGU: Vision GNN U-Net for Fast MRI
The majority of existing deep learning models, e. g., convolutional neural networks, work on data with Euclidean or regular grids structures.
A Three-Stage Self-Training Framework for Semi-Supervised Semantic Segmentation
The key idea of our technique is the extraction of the pseudo-masks statistical information to decrease uncertainty in the predicted probability whilst enforcing segmentation consistency in a multi-task fashion.
TFPnP: Tuning-free Plug-and-Play Proximal Algorithm with Applications to Inverse Imaging Problems
In this work, we present a class of tuning-free PnP proximal algorithms that can determine parameters such as denoising strength, termination time, and other optimization-specific parameters automatically.
Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problems
Moreover, we discuss the practical considerations of the plugged denoisers, which together with our learned policy yield state-of-the-art results.
CycleCluster: Modernising Clustering Regularisation for Deep Semi-Supervised Classification
Demonstrating that direct implementation of the cluster assumption is a viable alternative to the popular consistency based regularisation.
Single image reflection removal via learning with multi-image constraints
Reflections are very common phenomena in our daily photography, which distract people's attention from the scene behind the glass.
Superpixel Contracted Graph-Based Learning for Hyperspectral Image Classification
A central problem in hyperspectral image classification is obtaining high classification accuracy when using a limited amount of labelled data.
Semi-supervised Learning with Graphs: Covariance Based Superpixels For Hyperspectral Image Classification
In this paper, we present a graph-based semi-supervised framework for hyperspectral image classification.
