dgl
SuperGluePretrainedNetwork
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| dgl | SuperGluePretrainedNetwork | |
|---|---|---|
| 4 | 5 | |
| 12,999 | 2,906 | |
| 1.5% | 0.0% | |
| 9.9 | 0.0 | |
| 4 days ago | over 1 year ago | |
| Python | Python | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
dgl
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[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
For graph embeddings, there's quite a few. I'd recommend this one, but there's also this one (disclaimer: I'm the author) or this one, more of a DGL library.
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Detecting Out-of-Distribution Datapoints via Embeddings or Predictions
For trees/graphs, you’ll want a neural net that can take these as inputs for which I’m not sure a standard library exists. One recommendation is to checkout dgl: https://github.com/dmlc/dgl
- Beyond Message Passing: A Physics-Inspired Paradigm for Graph Neural Networks
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[D] Convenient libs to use for new research project at the intersection of GNN and RL.
The best pkg for GCN - https://github.com/dmlc/dgl
SuperGluePretrainedNetwork
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SuperGlue is a CVPR2022 research project done at Magicleap for pose estimation in real-world environments. Check out the tool link in the comments
Code: https://github.com/magicleap/SuperGluePretrainedNetwork
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Advances in SLAM since 2016
This basically includes a deep learning based approach to do keypoint detection, and match them across image frames. This includes papers like SuperPoint, Superglue, and more. There is also a way to do dense matching with neural networks.
- [D] Solo machine learning engineer woes
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How to train a CNN for a map localization task?
Feature matching is the way to go imo. Try out OpenCV's inbuilt feature matching methods like SIFT and FLANN. If the performance is poor, you can even try out CNN aided matching algos like SuperGlue Link (CVPR2020)
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What is the SOTA for feature extraction / description / matching ?
SIFT and brute force matching is your best bet in classical computer vision if you're unconcerned with runtime. There are methods from deep learning that can perform better, somewhat domain dependent. Check out superpoint and superglue from magic leap. https://github.com/magicleap/SuperGluePretrainedNetwork
What are some alternatives?
pytorch_geometric - Graph Neural Network Library for PyTorch
LoFTR - Code for "LoFTR: Detector-Free Local Feature Matching with Transformers", CVPR 2021, T-PAMI 2022
pytorch_geometric_temporal - PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
ORB_SLAM3 - ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM
torchdrug - A powerful and flexible machine learning platform for drug discovery
nerfmm - (Arxiv 2021) NeRF--: Neural Radiance Fields Without Known Camera Parameters
spektral - Graph Neural Networks with Keras and Tensorflow 2.
deep_gcns_torch - Pytorch Repo for DeepGCNs (ICCV'2019 Oral, TPAMI'2021), DeeperGCN (arXiv'2020) and GNN1000(ICML'2021): https://www.deepgcns.org
DeepLabCut - Official implementation of DeepLabCut: Markerless pose estimation of user-defined features with deep learning for all animals incl. humans
deodel - A mixed attributes predictive algorithm implemented in Python.
open_vins - An open source platform for visual-inertial navigation research.