A recurrent cross-frame attention module that injects historical context into coarse ground features improves cross-view visual localization accuracy on CVIS, KITTI-CVL, and real-vehicle tests.
EdgeUNet: Edge -guided multi-loss network for drivable area and lane segmentation in autonomous vehicles,
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
-
Cross-View Sequential Visual Localization with Spatio-Temporal Context Modeling for Autonomous Driving
A recurrent cross-frame attention module that injects historical context into coarse ground features improves cross-view visual localization accuracy on CVIS, KITTI-CVL, and real-vehicle tests.