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

Kai Chen

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}
}

You might also like...

[arXiv] What-If Motion Prediction for Autonomous Driving ❓🚗💨

WIMP - What If Motion Predictor Reference PyTorch Implementation for What If Motion Prediction [PDF] [Dynamic Visualizations] Setup Requirements The W

96 Dec 29, 2022

Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving

SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving Abstract In this paper, we introduce SalsaNext f

308 Jan 4, 2023

One Million Scenes for Autonomous Driving

ONCE Benchmark This is a reproduced benchmark for 3D object detection on the ONCE (One Million Scenes) dataset. The code is mainly based on OpenPCDet.

148 Dec 28, 2022

[ICCV'21] NEAT: Neural Attention Fields for End-to-End Autonomous Driving

NEAT: Neural Attention Fields for End-to-End Autonomous Driving Paper | Supplementary | Video | Poster | Blog This repository is for the ICCV 2021 pap

254 Jan 2, 2023

Code repository for Semantic Terrain Classification for Off-Road Autonomous Driving

BEVNet Datasets Datasets should be put inside data/. For example, data/semantic_kitti_4class_100x100. Training BEVNet-S Example: cd experiments bash t

24 Dec 12, 2022

An unofficial personal implementation of UM-Adapt, specifically to tackle joint estimation of panoptic segmentation and depth prediction for autonomous driving datasets.

Semisupervised Multitask Learning This repository is an unofficial and slightly modified implementation of UM-Adapt[1] using PyTorch. This code primar

11 Nov 25, 2022

Unofficial PyTorch implementation of "RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving" (ECCV 2020)

RTM3D-PyTorch The PyTorch Implementation of the paper: RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving (ECCV 2020

271 Nov 29, 2022

The Self-Supervised Learner can be used to train a classifier with fewer labeled examples needed using self-supervised learning.

Published by SpaceML • About SpaceML • Quick Colab Example Self-Supervised Learner The Self-Supervised Learner can be used to train a classifier with

92 Nov 30, 2022

Leveraging Instance-, Image- and Dataset-Level Information for Weakly Supervised Instance Segmentation

Leveraging Instance-, Image- and Dataset-Level Information for Weakly Supervised Instance Segmentation This paper has been accepted and early accessed

39 Sep 20, 2022

Owner

Kai Chen

CS PhD @ HKUST, Former CS @ FDU, RA @ IUB and Exchange Student@UoM

GitHub

Self-Supervised Pillar Motion Learning for Autonomous Driving (CVPR 2021)

Self-Supervised Pillar Motion Learning for Autonomous Driving Chenxu Luo, Xiaodong Yang, Alan Yuille Self-Supervised Pillar Motion Learning for Autono

101 Dec 5, 2022

[CVPR'21] Multi-Modal Fusion Transformer for End-to-End Autonomous Driving

TransFuser This repository contains the code for the CVPR 2021 paper Multi-Modal Fusion Transformer for End-to-End Autonomous Driving. If you find our

695 Jan 5, 2023

This solves the autonomous driving issue which is supported by deep learning technology. Given a video, it splits into images and predicts the angle of turning for each frame.

Self Driving Car An autonomous car (also known as a driverless car, self-driving car, and robotic car) is a vehicle that is capable of sensing its env

4 Sep 4, 2021

A PyTorch re-implementation of the paper 'Exploring Simple Siamese Representation Learning'. Reproduced the 67.8% Top1 Acc on ImageNet.

Exploring simple siamese representation learning This is a PyTorch re-implementation of the SimSiam paper on ImageNet dataset. The results match that

72 Nov 9, 2022

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

You might also like...

[arXiv] What-If Motion Prediction for Autonomous Driving ❓🚗💨

Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving

One Million Scenes for Autonomous Driving

[ICCV'21] NEAT: Neural Attention Fields for End-to-End Autonomous Driving

Code repository for Semantic Terrain Classification for Off-Road Autonomous Driving

An unofficial personal implementation of UM-Adapt, specifically to tackle joint estimation of panoptic segmentation and depth prediction for autonomous driving datasets.

Unofficial PyTorch implementation of "RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving" (ECCV 2020)

The Self-Supervised Learner can be used to train a classifier with fewer labeled examples needed using self-supervised learning.

Leveraging Instance-, Image- and Dataset-Level Information for Weakly Supervised Instance Segmentation

Owner

Kai Chen

Self-Supervised Pillar Motion Learning for Autonomous Driving (CVPR 2021)

[CVPR'21] Multi-Modal Fusion Transformer for End-to-End Autonomous Driving

This solves the autonomous driving issue which is supported by deep learning technology. Given a video, it splits into images and predicts the angle of turning for each frame.

A PyTorch re-implementation of the paper 'Exploring Simple Siamese Representation Learning'. Reproduced the 67.8% Top1 Acc on ImageNet.

PyTorch implementation of SimSiam: Exploring Simple Siamese Representation Learning

Exploring Simple Siamese Representation Learning

[CVPR 2022 Oral] Crafting Better Contrastive Views for Siamese Representation Learning

Repository to run object detection on a model trained on an autonomous driving dataset.

Official Repo for Ground-aware Monocular 3D Object Detection for Autonomous Driving

RTS3D: Real-time Stereo 3D Detection from 4D Feature-Consistency Embedding Space for Autonomous Driving