MultiSiam: Self-supervised Multi-instance Siamese Representation Learning for Autonomous Driving-Reference-Cited by-同舟云学术

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

Published:2021-10 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE/CVF International Conference on Computer Vision (ICCV)
language:
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Author:

Chen Kai¹,Hong Lanqing²,Xu Hang²,Li Zhenguo²,Yeung Dit-Yan¹

Affiliation:

1. Hong Kong University of Science and Technology

2. Huawei Noah’s Ark Lab

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9709627/9709628/09710311.pdf?arnumber=9710311

Reference32 articles.

1. A Survey of Autonomous Driving: Common Practices and Emerging Technologies

2. Bdd100k: A diverse driving video database with scalable annotation tooling;yu,2018

3. Large batch training of convolutional networks;you,2017

4. Bootstrap your own latent: A new approach to self-supervised learning;grill,2020

5. Soda10m: Towards large-scale object detection benchmark for autonomous driving;han,2021

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2. Label-efficient object detection via region proposal network pre-training;Neurocomputing;2024-04

3. Augmenting Reinforcement Learning With Transformer-Based Scene Representation Learning for Decision-Making of Autonomous Driving;IEEE Transactions on Intelligent Vehicles;2024-03

4. CalibNet: Dual-Branch Cross-Modal Calibration for RGB-D Salient Instance Segmentation;IEEE Transactions on Image Processing;2024

5. Invisible Backdoor Attacks on Key Regions Based on Target Neurons in Self-Supervised Learning;Lecture Notes in Computer Science;2024

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