  A central challenge in human pose estimation, as well as in many other
machine learning and prediction tasks, is the generalization problem. The
learned network does not have the capability to characterize the prediction
error, generate feedback information from the test sample, and correct the
prediction error on the fly for each individual test sample, which results in
degraded performance in generalization. In this work, we introduce a
self-correctable and adaptable inference (SCAI) method to address the
generalization challenge of network prediction and use human pose estimation as
an example to demonstrate its effectiveness and performance. We learn a
correction network to correct the prediction result conditioned by a fitness
feedback error. This feedback error is generated by a learned fitness feedback
network which maps the prediction result to the original input domain and
compares it against the original input. Interestingly, we find that this
self-referential feedback error is highly correlated with the actual prediction
error. This strong correlation suggests that we can use this error as feedback
to guide the correction process. It can be also used as a loss function to
quickly adapt and optimize the correction network during the inference process.
Our extensive experimental results on human pose estimation demonstrate that
the proposed SCAI method is able to significantly improve the generalization
capability and performance of human pose estimation.
