MLDAC:多任务密集注意计算自监督小样本分割方法OA北大核心

Aiming at the problem that existing few-shot semantic segmentation methods still need a large number of pixel-level annotations to complete the training of models,a multi-task dense attention computation self-supervised few-shot semantic segmentation method(MLDAC)is proposed.The method divides the saliency of a single image in the dataset into two parts,one part serves as the support image mask for few-shot segmentation,the other part or the all saliency respec-tively makes the cross-entropy loss of the prediction result as multiple targets for multi-task learning,improving model generalization.The Swin Transformer is used for the backbone network to extract feature maps at different levels.These feature maps are input into multiple levels of dense attention computation blocks to enhance pixel-level correspondence.The final prediction results are obtained by using the inter-scale mixing and feature skip-connection.The experimental results indicate that MLDAC attains 55.1%and 26.8%1-shot mIoU self-supervised few-shot segmentation on the PASCAL-5i and COCO-20i datasets respectively,compared with the current best self-supervised few-shot semantic seg-mentation method,improves by 1.3 and 2.2 percentage points respectively.In addition,the model achieves 78.1%1-shot mIoU on FSS-1000 dataset,verifying its efficacy.