Few-shot semantic segmentation can segment novel classes with only few examples.To address the problem of insufficient semantic information mining in existing methods,a method based on Dual Cross-Attention Network for few-shot image semantic segmentation is proposed.The method adopts Transformer structure and uses dual cross-attention modules to explore the remote dependencies between multi-scale query and support features from both channel and spatial dimensions.Firstly,a channel cross-attention module is proposed in combination with the position cross-attention module to form a dual cross-attention module.Wherein,the channel cross-attention module is applied to learn the channel semantic interrelationships between the query and support features.The position cross-attention module is used to capture the remote contextual correlations between the query and support features.Then,multi-scale interaction features containing rich semantic information can be provided to the query image by multiple dual cross-attention modules.Finally,to obtain accurate segmentation results,auxiliary supervision loss is introduced,and these multi-scale interaction features are connected to the decoder via upsampled and residual connection.The proposed method achieves 69.9%(1-shot)and 72.4%(5-shot)mIoU on the dataset PASCAL-5i,and 48.9%(1-shot)and 54.6%(5-shot)mIoU on the dataset COCO-20i,which attains the state-of-the-art segmentation performance in comparison with mainstream methods.
维普
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