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

Login Paper Search My Schedule Paper Index Help

My ICIP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDMLR-APPL-IP-2.6
Paper Title Learning Skip Map for Efficient Ultra-High Resolution Image Segmentation
Authors Pengcheng Pi, Ziyu Jiang, Zixiang Xiong, Texas A&M University, United States
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 The pattern distribution of ultra-high resolution images is usually unbalanced. While part of an image contains complex and fine-grained patterns such as boundaries, most areas are composed of simple and repeated patterns. In this work, we propose to learn a skip map, which can guide a segmentation network to skip simple patterns and hence reduce computational complexity. Specifically, the skip map highlights simple-pattern areas that can be down-sampled for processing at a lower resolution, while the remaining complex part is still segmented at the original resolution. We empirically show that the skip map can also reflect the quality of visual representations. Applied on the state-of-the-art ultra-high resolution image segmentation network, our proposed skip map saves more than 30% computation while maintaining comparable segmentation performance.