RoMa sets new state-of-the-art dense feature matching performance by fusing DINOv2 features with local ConvNet features, using anchor-probability transformer decoding, and regression-by-classification loss, with a 36% gain on WxBS.
Deep residual learning for image recognition
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2verdicts
UNVERDICTED 2representative citing papers
Hydra-MDP uses multi-teacher distillation and a multi-head decoder to learn diverse, metric-specific trajectories in an end-to-end autonomous-driving planner, winning the Navsim challenge.
citing papers explorer
-
Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation
Hydra-MDP uses multi-teacher distillation and a multi-head decoder to learn diverse, metric-specific trajectories in an end-to-end autonomous-driving planner, winning the Navsim challenge.