Human Pose Estimation with Iterative Error Feedback

Authors:
  • JCJoao Carreira
    Joao Carreira
  • PAPulkit Agrawal
    Pulkit Agrawal
  • KFKaterina Fragkiadaki
    Katerina Fragkiadaki
  • JMJitendra Malik
    Jitendra Malik
Top Cited Papers
Publisher website
Google Scholar
Abstract
Hierarchical feature extractors such as Convolutional Networks (ConvNets) have achieved impressive performance on a variety of classification tasks using purely feedforward processing. Feedforward architectures can learn rich representations of the input space but do not explicitly model dependencies in the output spaces, that are quite structured for tasks such as articulated human pose estimation or object segmentation. Here we propose a framework that expands the expressive power of hierarchical feature extractors to encompass both input and output spaces, by introducing top-down feedback. Instead of directly predicting the outputs in one go, we use a self-correcting model that progressively changes an initial solution by feeding back error predictions, in a process we call Iterative Error Feedback (IEF). IEF shows excellent performance on the task of articulated pose estimation in the challenging MPII and LSP benchmarks, matching the state-of-the-art without requiring ground truth scale annotation.

This publication has 29 references indexed in Scilit: