计算机科学
人工智能
分类器(UML)
对抗制
模态(人机交互)
特征(语言学)
模式识别(心理学)
编码器
机器学习
操作系统
哲学
语言学
作者
Zhengyi Chen,Chunmin Zhang,Biyun Zhang,Yifan He
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:62: 1-15
标识
DOI:10.1109/tgrs.2024.3354304
摘要
Supervised learning models have achieved remarkable success in the field of remote sensing, but their applicability is limited by the significant requirement of high-quality labeled data. This article presents the Triplet Adversarial Contrastive Learning (TACL) model, as a self-supervised feature extractor. Considering the potential contrastive semantic conflicts, which may occur due to the different descriptive abilities of various modalities, TACL constructs a triplet contrastive learning framework aligning the two original modalities and a fused modality. To augment the acquired representations of the model, TACL introduces an Adversarial Hard-negative Sample Generation strategy, aiming to boost the resemblance between the feature vectors of negative samples and anchors. Additionally, a ConvNeXt-based lightweight encoder is designed as the foundational backbone of the model, specifically enriching of the expression of central features. A series of few-shot classification experiments substantiate the exceptional performance of the features extracted by TACL, with the simplistic classifier SVM. As a label-free pre-training approach, TACL holds great potential for enhancing the performance of various multimodal remote sensing tasks in scenarios with limited label availability.
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