  Off-the-shelf single-stage multi-person pose regression methods generally
leverage the instance score (i.e., confidence of the instance localization) to
indicate the pose quality for selecting the pose candidates. We consider that
there are two gaps involved in existing paradigm:~1) The instance score is not
well interrelated with the pose regression quality.~2) The instance feature
representation, which is used for predicting the instance score, does not
explicitly encode the structural pose information to predict the reasonable
score that represents pose regression quality. To address the aforementioned
issues, we propose to learn the pose regression quality-aware representation.
Concretely, for the first gap, instead of using the previous instance
confidence label (e.g., discrete {1,0} or Gaussian representation) to denote
the position and confidence for person instance, we firstly introduce the
Consistent Instance Representation (CIR) that unifies the pose regression
quality score of instance and the confidence of background into a pixel-wise
score map to calibrates the inconsistency between instance score and pose
regression quality. To fill the second gap, we further present the Query
Encoding Module (QEM) including the Keypoint Query Encoding (KQE) to encode the
positional and semantic information for each keypoint and the Pose Query
Encoding (PQE) which explicitly encodes the predicted structural pose
information to better fit the Consistent Instance Representation (CIR). By
using the proposed components, we significantly alleviate the above gaps. Our
method outperforms previous single-stage regression-based even bottom-up
methods and achieves the state-of-the-art result of 71.7 AP on MS COCO test-dev
set.
