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- Title
Few-shot segmentation with duplex network and attention augmented module.
- Authors
Sifu Zeng; Jie Yang; Wang Luo; Yudi Ruan
- Abstract
Establishing the relationship between a limited number of samples and segmented objects in diverse scenarios is the primary challenge in few-shot segmentation. However, many previous works overlooked the crucial support-query set interaction and the deeper information that needs to be explored. This oversight can lead to model failure when confronted with complex scenarios, such as ambiguous boundaries. To solve this problem, a duplex network that utilizes the suppression and focus concept is proposed to effectively suppress the background and focus on the foreground. Our network includes dynamic convolution to enhance the support-query interaction and a prototype match structure to fully extract information fromsupport and query. The proposedmodel is called dynamic prototype mixture convolutional networks (DPMC). To minimize the impact of redundant information, we have incorporated a hybrid attentional module called double-layer attention augmented convolutional module (DAAConv) into DPMC. This module enables the network to concentrate more on foreground information. Our experiments on PASCAL-5i and COCO-20i datasets suggested that DPMC and DAAConv outperform traditional prototype-based methods by up to 5-8% on average.
- Subjects
PROBLEM solving
- Publication
Frontiers in Neurorobotics, 2023, p1
- ISSN
1662-5218
- Publication type
Article
- DOI
10.3389/fnbot.2023.1206189