Hybrid feature enhancement network for few-shot semantic segmentation

作者：Min, Hai; Zhang, Yemao; Zhao, Yang^*; Jia, Wei; Lei, Yingke; Fan, Chunxiao

来源：Pattern Recognition, 2023, 137: 109291.

DOI：10.1016/j.patcog.2022.109291

• Semantic segmentation
• Few -shot segmentation
• Few -shot learning

Although few-shot semantic segmentation methods have been widely studied in computer vision field, it still has room for improvement. In this work, we propose to enrich the feature representation with texture information and assign adaptive weights to losses. Specially, we incorporate the texture information obtained by texture enhance module with layer's features on ResNet, and then get a series of hybrid features. The incorporation of texture information enhances the similarity calculation to make the support set guidance more effective. Besides, the proposed adaptive loss makes the network optimize in a better direction. The experiments testify that the proposed method achieves the better results than that of previous methods on few-shot segmentation dataset such as PASCAL-5 i , COCO-20 i and FSS-10 0 0.

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