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-IVASR-1.9
Paper Title IMAGE-LEVEL SUPERVISED INSTANCE SEGMENTATION USING INSTANCE-WISE BOUNDARY
Authors Yuyuan Yang, Ya-Li Hou, Beijing Jiaotong University, China; Zhijiang Hou, Tianjin University of Technology, China; Xiaoli Hao, Yan Shen, Beijing Jiaotong University, China
SessionMLR-APPL-IVASR-1: Machine learning for image and video analysis, synthesis, and retrieval 1
LocationArea D
Session Time:Monday, 20 September, 13:30 - 15:00
Presentation Time:Monday, 20 September, 13:30 - 15:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image & video analysis, synthesis, and retrieval
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Recently, most image-level supervised instance segmentation methods extend Class Attention Maps (CAMs) to find the entire instance masks. Inter-pixel Relation Network (IRNet) can effectively generate the class-wise boundary maps for attention score propagation. However, class-wise boundary is likely to cause the failure of segmentation among instances. In this work, we find instance-wise information can be extracted from the displacement field of IRNet. Motivated by the observations, an improved IRNet-based instance segmentation method with instance-wise boundary has been developed. Experimental results based on PASCAL VOC 2012 demonstrate the effectiveness of our proposed method. Compared with the recent state-of-the-art methods, the mean average precision can be increased by 4.3% without any additional annotations.