2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IP-2.5
Paper Title LEARNING WITH MEMORY FOR FEW-SHOT SEMANTIC SEGMENTATION
Authors Hongchao Lu, Chao Wei, Tsinghua University, China; Zhidong Deng, Tsinghua university, China
SessionMLR-APPL-IP-2: Machine learning for image processing 2
LocationArea E
Session Time:Monday, 20 September, 15:30 - 17:00
Presentation Time:Monday, 20 September, 15:30 - 17:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Despite great progress made in the few-shot semantic segmentation task, the existing works still suffer from problems of incompleteness and inconsistency of segmentation. In this paper, a novel attention-aided LSTM optimization network called LONet is proposed, which optimizes predictions without forgetting useful inner cues. Particularly, we calculate an attention map to align and match possible locations with query features to deal with incomplete segmentation. Then, an LSTM-based module is designed to overcome the segmentation inconsistency by memorizing and updating useful cues iteratively. Extensive experiments are conducted on two popular few-shot segmentation datasets including PASCAL-5i and FSS-1000. The experimental results on the FSS-1000 dataset demonstrate that our LONet exceeds the state-of-the-art results by 2.1% and 2.3%, respectively.