Distance regression
4 papers with code • 2 benchmarks • 2 datasets
Prediction of the distance between connected nodes in molecular/material/nanomaterial graphs.
CHILI-3K
Most implemented papers
PolarMask: Single Shot Instance Segmentation with Polar Representation
xieenze/PolarMask
•
•
CVPR 2020
In this paper, we introduce an anchor-box free and single shot instance segmentation method, which is conceptually simple, fully convolutional and can be used as a mask prediction module for instance segmentation, by easily embedding it into most off-the-shelf detection methods.
EOLO: Embedded Object Segmentation only Look Once
zlf1993/cy
•
•
In this paper, we introduce an anchor-free and single-shot instance segmentation method, which is conceptually simple with 3 independent branches, fully convolutional and can be used by easily embedding it into mobile and embedded devices.
DistFormer: Enhancing Local and Global Features for Monocular Per-Object Distance Estimation
Existing approaches rely on two scales: local information (i. e., the bounding box proportions) or global information, which encodes the semantics of the scene as well as the spatial relations with neighboring objects.
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).
