Position regression
7 papers with code • 2 benchmarks • 2 datasets
Prediction of the absolute 3D position of nodes in molecular/material/nanomaterial graphs.
CHILI-3K
Most implemented papers
Contrastive Learning of Relative Position Regression for One-Shot Object Localization in 3D Medical Images
LWHYC/RPR-Loc
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To address this problem, we present a one-shot framework for organ and landmark localization in volumetric medical images, which does not need any annotation during the training stage and could be employed to locate any landmarks or organs in test images given a support (reference) image during the inference stage.
MobRecon: Mobile-Friendly Hand Mesh Reconstruction from Monocular Image
SeanChenxy/HandMesh
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CVPR 2022
In this work, we propose a framework for single-view hand mesh reconstruction, which can simultaneously achieve high reconstruction accuracy, fast inference speed, and temporal coherence.
Denoised MDPs: Learning World Models Better Than the World Itself
facebookresearch/denoised_mdp
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The ability to separate signal from noise, and reason with clean abstractions, is critical to intelligence.
SD-LayerNet: Semi-supervised retinal layer segmentation in OCT using disentangled representation with anatomical priors
Optical coherence tomography (OCT) is a non-invasive 3D modality widely used in ophthalmology for imaging the retina.
Position Regression for Unsupervised Anomaly Detection
cian.unibas.ch/position-regression
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Most current anomaly detection methods for medical images are based on image reconstruction.
Online Transformers with Spiking Neurons for Fast Prosthetic Hand Control
nathanleroux-git/onlinetransformerwithspikingneurons
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However, the self-attention mechanism often used in Transformers requires large time windows for each computation step and thus makes them less suitable for online signal processing compared to Recurrent Neural Networks (RNNs).
CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning
We invite the graph ML community to address these open challenges by presenting two new chemically-informed large-scale inorganic (CHILI) nanomaterials datasets: A medium-scale dataset (with overall >6M nodes, >49M edges) of mono-metallic oxide nanomaterials generated from 12 selected crystal types (CHILI-3K) and a large-scale dataset (with overall >183M nodes, >1. 2B edges) of nanomaterials generated from experimentally determined crystal structures (CHILI-100K).
