An Active and Contrastive Learning Framework for Fine-Grained Off-Road Semantic Segmentation

Author:

Gao Biao1ORCID,Zhao Xijun2ORCID,Zhao Huijing1ORCID

Affiliation:

1. Key Laboratory of Machine Perception (MOE), Peking University, Beijing, China

2. China North Artificial Intelligence and Innovation Research Institute, Beijing, China

Funder

National Natural Science Foundation of China

High-Performance Computing Platform of Peking University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Real-Time Terrain Condition Detection for Off-Road Driving Based on Transformer;IEEE Transactions on Intelligent Transportation Systems;2024-09

2. Passable area segmentation for open-pit mine road from vehicle perspective;Engineering Applications of Artificial Intelligence;2024-03

3. Semantic Segmentation in Thermal Videos: A New Benchmark and Multi-Granularity Contrastive Learning-Based Framework;IEEE Transactions on Intelligent Transportation Systems;2023-12

4. Saliency-Assisted Collaborative Learning Network for Road Scene Semantic Segmentation;KSII Transactions on Internet and Information Systems;2023-03-31

5. A Survey of Deep Active Learning for Foundation Models;Intelligent Computing;2023-01

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