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Found 3 papers, 1 papers with code
SGV3D:Towards Scenario Generalization for Vision-based Roadside 3D Object Detection
Our method surpasses all previous methods by a significant margin in new scenes, including +42. 57% for vehicle, +5. 87% for pedestrian, and +14. 89% for cyclist compared to BEVHeight on the DAIR-V2X-I heterologous benchmark.
Deep Graph Reprogramming
In this paper, we explore a novel model reusing task tailored for graph neural networks (GNNs), termed as "deep graph reprogramming".
Accelerating Fair Federated Learning: Adaptive Federated Adam
This is known as the fairness problem in federated learning.
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