Jun 30, 2022 · Abstract: Detection models trained by one party (server) may face severe performance degradation when distributed to other users (clients).
We propose a cross-domain federated object detection framework, named FedOD. The proposed framework first performs the federated training to obtain a public ...
We establish a federated object detection dataset which has significant background differences and instance differences based on multiple public autonomous.
This paper proposes a cross-domain federated object detection framework, named FedOD, which first performs the federated training to obtain a public global ...
Aug 28, 2023 · In this paper, we focus on a special cross-domain scenario in which theserver has large-scale labeled data and multiple clients only have a ...
Cross domain object detection is a realistic and challenging task in the wild. It suffers from performance degradation.
Missing: Federated | Show results with:Federated
May 23, 2023 · We propose an adaptive FL algorithm, called FedDAD (Federated Domain Adaptive Detector), which aggregates models with dynamic attention targeting the ...
Missing: Cross- | Show results with:Cross-
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This paper presents a Semi-Supervised Federated Object Detection (SSFOD) framework featuring a two-stage training strategy, FedSTO, designed to address the ...
In this paper, we focus on a special cross-domain scenario where the server contains large-scale data and multiple clients only contain a small amount of data; ...
Dec 18, 2021 · We evaluated cross-domain federated learning for the tasks of object detection and segmentation across two different experimental settings.