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Found 3 papers, 1 papers with code
UniHDA: A Unified and Versatile Framework for Multi-Modal Hybrid Domain Adaptation
In this paper, we propose UniHDA, a \textbf{unified} and \textbf{versatile} framework for generative hybrid domain adaptation with multi-modal references from multiple domains.
Pseudo Label Refinery for Unsupervised Domain Adaptation on Cross-dataset 3D Object Detection
Specifically, in the selection process, to improve the reliability of pseudo boxes, we propose a complementary augmentation strategy.
G2LTraj: A Global-to-Local Generation Approach for Trajectory Prediction
Finally, to ensure the optimal granularity of key steps, we design a selectable granularity strategy that caters to each predicted trajectory.
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