  We build a deep neural network based on the Mask R-CNN framework to detect
the Higgs jets and top quark jets in any event image. We propose an algorithm
to assign the top quark final states at the ground truth level so that the
network can be trained in a supervised manner. A new jet branch is added to the
network, which uses constituent information to predict the four-momenta of the
original parton, thus intrinsically implementing the pileup mitigation. The
network can predict both the shapes and the momenta of target jets. We show
that the network surpasses the LorentzNet in top and Higgs tagging and the
PELICAN network in momentum regression for certain cases, in terms of
reconstruction efficiency and accuracy. We also show that the performance of
the network does not degrade much when applied to events of a process different
from the trained one and to events with overlapping jets.
