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PGFed: Personalize Each Client's Global Objective for Federated Learning.

, , , and . ICCV, page 3923-3933. IEEE, (2023)

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A Lightweight Graph Transformer Network for Human Mesh Reconstruction from 2D Human Pose., , , , and . ACM Multimedia, page 5496-5507. ACM, (2022)Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning., , , , , and . CoRR, (2021)PGFed: Personalize Each Client's Global Objective for Federated Learning., , , and . CoRR, (2022)A Lightweight Graph Transformer Network for Human Mesh Reconstruction from 2D Human Pose., , , , and . CoRR, (2021)Mez: An Adaptive Messaging System for Latency-Sensitive Multi-Camera Machine Vision at the IoT Edge., , , and . IEEE Access, (2021)REVAMP2T: Real-time Edge Video Analytics for Multi-camera Privacy-aware Pedestrian Tracking., , , , , and . CoRR, (2019)REVAMP2T: Real-Time Edge Video Analytics for Multicamera Privacy-Aware Pedestrian Tracking., , , , , and . IEEE Internet Things J., 7 (4): 2591-2602 (2020)FeatER: An Efficient Network for Human Reconstruction via Feature Map-Based TransformER., , , , and . CVPR, page 13945-13954. IEEE, (2023)PGFed: Personalize Each Client's Global Objective for Federated Learning., , , and . ICCV, page 3923-3933. IEEE, (2023)3D Human Pose Estimation with Spatial and Temporal Transformers., , , , , and . ICCV, page 11636-11645. IEEE, (2021)