Federated Contrastive Domain Adaptation for Category-inconsistent Object Detection

Wei-Yu Chen, Peggy Joy Lu, Vincent Shin-Mu Tseng

Abstract: To obtain diverse scenarios for collaboratively training a more generalized object detector, the multi-source domain adaptive object detection has been proposed. However, such scenario faces challenges related to data privacy, domain discrepancy and category inconsistency, thus we propose a framework called FedCoin: Federated Contrastive domain-adaptation for category-inconsistent object detection. On the client sides, a novel dynamic model contrastive strategy is proposed to reduce excessively domain-specific features from local models. On the server side, we design a two-stage teacher-student architecture to tackle the challenge of backbone aggregation and inconsistent categories integration. Our method outperforms SOTA methods across different domain adaptation tasks, with an average precision increase of 6% on various datasets, demonstrating its superiority over existing methods for category-inconsistent and privacy-preserving scenarios. The source code is available online: https://github.com/ccuvislab/FedCoin

External IDs:dblp:conf/vcip/ChenLT24