  In multi-person pose estimation, the left/right joint type discrimination is
always a hard problem because of the similar appearance. Traditionally, we
solve this problem by stacking multiple refinement modules to increase
network's receptive fields and capture more global context, which can also
increase a great amount of computation. In this paper, we propose a Multi-level
Network (MLN) that learns to aggregate features from lower-level (left/right
information), upper-level (localization information), joint-limb level
(complementary information) and global-level (context) information for
discrimination of joint type. Through feature reuse and its intra-relation, MLN
can attain comparable performance to other conventional methods while runtime
speed retains at 42.2 FPS.

Computer Vision and Pattern Recognition