MultiSiam: Self-supervised Multi-instance Siamese Representation Learning for Autonomous Driving

Last update: Apr 19, 2022

Related tags

Deep Learning MultiSiam

Overview

MultiSiam: Self-supervised Multi-instance Siamese Representation Learning for Autonomous Driving

Code will be available soon.

Motivation

Architecture

Experiment

Pre-train on multi-instance datasets

Pre-train on single-instance ImageNet

Citation

@inproceedings{chen2021multisiam,
  title={MultiSiam: Self-supervised Multi-instance Siamese Representation Learning for Autonomous Driving},
  author={Chen, Kai and Hong, Lanqing and Xu, Hang and Li, Zhenguo and Yeung, Dit-Yan},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={7546--7554},
  year={2021}
}

Owner

Kai Chen

CS PhD @ HKUST, Former CS @ FDU, RA @ IUB and Exchange [email protected]

GitHub Repository

Automatic labeling, conversion of different data set formats, sample size statistics, model cascade

Simple Gadget Collection for Object Detection Tasks Automatic image annotation Conversion between different annotation formats Obtain statistical info

4 Aug 24, 2022

Official implementation for (Show, Attend and Distill: Knowledge Distillation via Attention-based Feature Matching, AAAI-2021)

Show, Attend and Distill: Knowledge Distillation via Attention-based Feature Matching Official pytorch implementation of "Show, Attend and Distill: Kn

80 Dec 16, 2022

deep learning for image processing including classification and object-detection etc.

深度学习在图像处理中的应用教程前言本教程是对本人研究生期间的研究内容进行整理总结，总结的同时也希望能够帮助更多的小伙伴。后期如果有学习到新的知识也会与大家一起分享。本教程会以视频的方式进行分享，教学流程如下： 1）介绍网络的结构与创新点 2）使用Pytorch进行网络的搭建与训练 3）使用Te

13.6k Jan 04, 2023

Code for the paper "JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design"

JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design This repository contains code for the paper: JA

55 Nov 29, 2022

Tools for investing in Python

InvestOps Original repository on GitHub Original author is Magnus Erik Hvass Pedersen Introduction This is a Python package with simple and effective

24 Nov 26, 2022

Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes

Neural Scene Flow Fields PyTorch implementation of paper "Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes", CVPR 2021 [Projec

583 Dec 30, 2022

PyTorch implementation of EfficientNetV2

[NEW!] Check out our latest work involution accepted to CVPR'21 that introduces a new neural operator, other than convolution and self-attention. PyTo

375 Jan 03, 2023

A machine learning library for spiking neural networks. Supports training with both torch and jax pipelines, and deployment to neuromorphic hardware.

Rockpool Rockpool is a Python package for developing signal processing applications with spiking neural networks. Rockpool allows you to build network

21 Dec 14, 2022

Spectralformer: Rethinking hyperspectral image classification with transformers

Spectralformer: Rethinking hyperspectral image classification with transformers Danfeng Hong, Zhu Han, Jing Yao, Lianru Gao, Bing Zhang, Antonio Plaza

102 Dec 29, 2022

Eth brownie struct encoding example

eth-brownie struct encoding example Overview This repository contains an example of encoding a struct, so that it can be used in a function call, usin

2 Mar 04, 2022

Repo for paper "Dynamic Placement of Rapidly Deployable Mobile Sensor Robots Using Machine Learning and Expected Value of Information"

Repo for paper "Dynamic Placement of Rapidly Deployable Mobile Sensor Robots Using Machine Learning and Expected Value of Information" Notes I probabl

0 Jul 01, 2021

PyTorch implementation of "Contrast to Divide: self-supervised pre-training for learning with noisy labels"

Contrast to Divide: self-supervised pre-training for learning with noisy labels This is an official implementation of "Contrast to Divide: self-superv

55 Nov 23, 2022

You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling

You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling Transformer-based models are widely used in natural language processi

12 Jan 01, 2023

Machine-in-the-Loop Rewriting for Creative Image Captioning

Machine-in-the-Loop Rewriting for Creative Image Captioning Data Annotated sources of data used in the paper: Data Source URL Mohammed et al. Link Gor

6 Jul 24, 2022

Models Supported: AlbUNet [18, 34, 50, 101, 152] (1D and 2D versions for Single and Multiclass Segmentation, Feature Extraction with supports for Deep Supervision and Guided Attention)

AlbUNet-1D-2D-Tensorflow-Keras This repository contains 1D and 2D Signal Segmentation Model Builder for AlbUNet and several of its variants developed

1 Nov 15, 2021

MultiSiam: Self-supervised Multi-instance Siamese Representation Learning for Autonomous Driving

Related tags

Overview

MultiSiam: Self-supervised Multi-instance Siamese Representation Learning for Autonomous Driving

Motivation

Architecture

Experiment

Pre-train on multi-instance datasets

Pre-train on single-instance ImageNet

Citation

Owner

Kai Chen

Automatic labeling, conversion of different data set formats, sample size statistics, model cascade

Official implementation for (Show, Attend and Distill: Knowledge Distillation via Attention-based Feature Matching, AAAI-2021)

deep learning for image processing including classification and object-detection etc.

Code for the paper "JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design"

Tools for investing in Python

Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes

PyTorch implementation of EfficientNetV2

A machine learning library for spiking neural networks. Supports training with both torch and jax pipelines, and deployment to neuromorphic hardware.

Spectralformer: Rethinking hyperspectral image classification with transformers

Eth brownie struct encoding example

Repo for paper "Dynamic Placement of Rapidly Deployable Mobile Sensor Robots Using Machine Learning and Expected Value of Information"

PyTorch implementation of "Contrast to Divide: self-supervised pre-training for learning with noisy labels"

You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling

Machine-in-the-Loop Rewriting for Creative Image Captioning

Models Supported: AlbUNet [18, 34, 50, 101, 152] (1D and 2D versions for Single and Multiclass Segmentation, Feature Extraction with supports for Deep Supervision and Guided Attention)

Sinkformers: Transformers with Doubly Stochastic Attention

An end-to-end PyTorch framework for image and video classification

Julia package for multiway (inverse) covariance estimation.

My personal code and solution to the Synacor Challenge from 2012 OSCON.

Deep Learning and Logical Reasoning from Data and Knowledge