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-IVSMR-3.6
Paper Title Iterative Subnetwork with Linear Hierarchical Ordering for Human Pose Estimation
Authors Shek Wai Chu, Chaoyi Zhang, University of Sydney, Australia; Yang Song, University of New South Wales, Australia; Weidong Cai, University of Sydney, Australia
SessionMLR-APPL-IVSMR-3: Machine learning for image and video sensing, modeling and representation 3
LocationArea D
Session Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image & video sensing, modeling, and representation
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Human pose estimation is a long-standing and challenging problem in computer vision. Many recent advancements in the field have relied on complex structure refinement and specific human joint graphical relations. However, progress has been saturated in terms of accuracy. Each time, new state-of-the-art approaches only improve accuracy by less than 0.3% in the MPII test set despite using complicated model structures. Most recent developments can be summarized into two main ideas: 1) refinement subnetwork to improve predictions iteratively and 2) exploitation of human joint graphical relations. In this work, we present how efficient and simple iterative subnetworks with linear hierarchical ordering based on the aforementioned ideas can help to improve accuracy on strong backbone models. Different versions of iterative subnetwork are examined. Significant improvements on difficult body part predictions such as wrists and ankles using simple convolution subnetwork are observed. Further improvements can be made by using a large receptive field subnetwork such as axial-transformer.