UDAPose improves low-light human pose estimation by synthesizing realistic images via DHF and LCIM modules and dynamically balancing image cues with pose priors using DCA, yielding AP gains of 10.1 and 7.4 over prior methods.
Squeeze-and-excitation networks.IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(8):2011– 2023
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
UDAPose: Unsupervised Domain Adaptation for Low-Light Human Pose Estimation
UDAPose improves low-light human pose estimation by synthesizing realistic images via DHF and LCIM modules and dynamically balancing image cues with pose priors using DCA, yielding AP gains of 10.1 and 7.4 over prior methods.