Code for "Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and Tracking of Object Poses in 3D Space"

Related tags

Deep LearningSS-Conv
Overview

Sparse Steerable Convolution (SS-Conv)

Code for "Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and Tracking of Object Poses in 3D Space", NeurIPS 2021.

Created by Jiehong Lin, Hongyang Li, Ke Chen, Jiangbo Lu, and Kui Jia.

Owner
Research lab focusing on CV, ML, and AI
GitHub Repository
Implementation of Axial attention - attending to multi-dimensional data efficiently

Axial Attention Implementation of Axial attention in Pytorch. A simple but powerful technique to attend to multi-dimensional data efficiently. It has

Phil Wang 250 Dec 25, 2022
Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product Operators

Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product Operators This is our Pytorch implementation for t

RUCAIBox 12 Jul 22, 2022
🐸STT integration examples

🐸 STT 0.9.x Examples These are various examples on how to use or integrate 🐸 STT using our packages. It is a good way to just try out 🐸 STT before

coqui 92 Dec 19, 2022
Improving Deep Network Debuggability via Sparse Decision Layers

Improving Deep Network Debuggability via Sparse Decision Layers This repository contains the code for our paper: Leveraging Sparse Linear Layers for D

Madry Lab 35 Nov 14, 2022
Seach Losses of our paper 'Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search', accepted by ICLR 2021.

CSE-Autoloss Designing proper loss functions for vision tasks has been a long-standing research direction to advance the capability of existing models

Peidong Liu(刘沛东) 54 Dec 17, 2022
Fast and Easy Infinite Neural Networks in Python

Neural Tangents ICLR 2020 Video | Paper | Quickstart | Install guide | Reference docs | Release notes Overview Neural Tangents is a high-level neural

Google 1.9k Jan 09, 2023
A library for graph deep learning research

Documentation | Paper [JMLR] | Tutorials | Benchmarks | Examples DIG: Dive into Graphs is a turnkey library for graph deep learning research. Why DIG?

DIVE Lab, Texas A&M University 1.3k Jan 01, 2023
POPPY (Physical Optics Propagation in Python) is a Python package that simulates physical optical propagation including diffraction

POPPY: Physical Optics Propagation in Python POPPY (Physical Optics Propagation in Python) is a Python package that simulates physical optical propaga

Space Telescope Science Institute 132 Dec 15, 2022
Editing a Conditional Radiance Field

Editing Conditional Radiance Fields Project | Paper | Video | Demo Editing Conditional Radiance Fields Steven Liu, Xiuming Zhang, Zhoutong Zhang, Rich

Steven Liu 216 Dec 30, 2022
Task-based end-to-end model learning in stochastic optimization

Task-based End-to-end Model Learning in Stochastic Optimization This repository is by Priya L. Donti, Brandon Amos, and J. Zico Kolter and contains th

CMU Locus Lab 164 Dec 29, 2022
A Decentralized Omnidirectional Visual-Inertial-UWB State Estimation System for Aerial Swar.

Omni-swarm A Decentralized Omnidirectional Visual-Inertial-UWB State Estimation System for Aerial Swarm Introduction Omni-swarm is a decentralized omn

HKUST Aerial Robotics Group 99 Dec 23, 2022
Reaction SMILES-AA mapping via language modelling

rxn-aa-mapper Reactions SMILES-AA sequence mapping setup conda env create -f conda.yml conda activate rxn_aa_mapper In the following we consider on ex

16 Dec 13, 2022
Code for "Steerable Pyramid Transform Enables Robust Left Ventricle Quantification"

Code for "Steerable Pyramid Transform Enables Robust Left Ventricle Quantification" This is an end-to-end framework for accurate and robust left ventr

2 Jul 09, 2022
This project is based on RIFE and aims to make RIFE more practical for users by adding various features and design new models

CPM 项目描述 CPM（Chinese Pretrained Models）模型是北京智源人工智能研究院和清华大学发布的中文大规模预训练模型。官方发布了三种规模的模型，参数量分别为109M、334M、2.6B，用户需申请与通过审核，方可下载。 由于原项目需要考虑大模型的训练和使用，需要安装较为复杂

hzwer 190 Jan 08, 2023
Given a 2D triangle mesh, we could randomly generate cloud points that fill in the triangle mesh

generate_cloud_points Given a 2D triangle mesh, we could randomly generate cloud points that fill in the triangle mesh. Run python disp_mesh.py Or you

Peng Yu 2 Dec 24, 2021
A 1.3B text-to-image generation model trained on 14 million image-text pairs

minDALL-E on Conceptual Captions minDALL-E, named after minGPT, is a 1.3B text-to-image generation model trained on 14 million image-text pairs for no

Kakao Brain 604 Dec 14, 2022
An implementation of MobileFormer

MobileFormer An implementation of MobileFormer proposed by Yinpeng Chen, Xiyang Dai et al. Including [1] Mobile-Former proposed in:

slwang9353 62 Dec 28, 2022
Efficient Two-Step Networks for Temporal Action Segmentation (Neurocomputing 2021)

Efficient Two-Step Networks for Temporal Action Segmentation This repository provides a PyTorch implementation of the paper Efficient Two-Step Network

8 Apr 16, 2022
This repo is to present various code demos on how to use our Graph4NLP library.

Deep Learning on Graphs for Natural Language Processing Demo The repository contains code examples for DLG4NLP tutorials at NAACL 2021, SIGIR 2021, KD

Graph4AI 143 Dec 23, 2022
NIMA: Neural IMage Assessment

PyTorch NIMA: Neural IMage Assessment PyTorch implementation of Neural IMage Assessment by Hossein Talebi and Peyman Milanfar. You can learn more from

Kyryl Truskovskyi 293 Dec 30, 2022