融合提示学习与分类确定性最大化的领域自适应OA

Domain adaptation faced the issue of complex and variable real-world scenarios,and existing methods mostly focused on optimizing classification consistency while neglecting classification certainty.To address these issues,a network model combining constrastive language-image pre-training(CLIP)with classification certainty maximization was proposed.CLIP,as a multimodal pre-trained model,was pre-trained on a large scale of image-text pairs and possessed strong cross-domain generalization capabili-ties.By leveraging prompt learning and contrastive learning,the knowledge of the CLIP model was ac-quired,enabling the model to adapt more complex real-world scenarios.Through the method of classifica-tion certainty maximization,a dual-classifier was employed to assess classification consistency and reduce confusion during the model's inference process.Experiments were conducted on three domain adaptation benchmark datasets:Office-31,Office-Home,and MiniDomainNet.The experimental results indicated that compared with existing advanced methods,the proposed model showed improvements in image classi-fication accuracy across all three datasets.