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
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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.
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RoMa: Robust Dense Feature Matching
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.
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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.