<ul><li>Unsupervised Waste Classification By Dual-Encoder Contrastive Learning and Multi-Clustering Voting (DECMCV)</li><li>Constructed a bias-free and cost-effective unsupervised waste classification method called DECMCV.</li><li>Utilizes a pre-trained ConvNeXt model for image encoding and VisionTransformer for generating positive samples.</li><li>DECMCV achieves high accuracies on TrashNet and Huawei Cloud datasets, improving classification accuracy by 29.85% compared to supervised models.</li></ul>

Unsupervised Waste Classification By Dual-Encoder Contrastive Learning and Multi-Clustering Voting (DECMCV)

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