Jie Zhang

Sikai Bai, Shuaicheng Li, Weiming Zhuang et al.

Federated learning has become a popular method to learn from decentralized heterogeneous data. Federated semi-supervised learning (FSSL) emerges to train models from a small fraction of labeled data due to label scarcity on decentralized clients. Existing FSSL methods assume independent and identically distributed (IID) labeled data across clients and consistent class distribution between labeled and unlabeled data within a client. This work studies a more practical and challenging scenario of FSSL, where data distribution is different not only across clients but also within a client between labeled and unlabeled data. To address this challenge, we propose a novel FSSL framework with dual regulators, FedDure. FedDure lifts the previous assumption with a coarse-grained regulator (C-reg) and a fine-grained regulator (F-reg): C-reg regularizes the updating of the local model by tracking the learning effect on labeled data distribution; F-reg learns an adaptive weighting scheme tailored for unlabeled instances in each client. We further formulate the client model training as bi-level optimization that adaptively optimizes the model in the client with two regulators. Theoretically, we show the convergence guarantee of the dual regulators. Empirically, we demonstrate that FedDure is superior to the existing methods across a wide range of settings, notably by more than 11 on CIFAR-10 and CINIC-10 datasets.

Jie Zhang, Pengcheng Shi, Zaiwang Gu et al.

The neural rendering of humans is a topic of great research significance. However, previous works mostly focus on achieving photorealistic details, neglecting the exploration of human parsing. Additionally, classical semantic work are all limited in their ability to efficiently represent fine results in complex motions. Human parsing is inherently related to radiance reconstruction, as similar appearance and geometry often correspond to similar semantic part. Furthermore, previous works often design a motion field that maps from the observation space to the canonical space, while it tends to exhibit either underfitting or overfitting, resulting in limited generalization. In this paper, we present Semantic-Human, a novel method that achieves both photorealistic details and viewpoint-consistent human parsing for the neural rendering of humans. Specifically, we extend neural radiance fields (NeRF) to jointly encode semantics, appearance and geometry to achieve accurate 2D semantic labels using noisy pseudo-label supervision. Leveraging the inherent consistency and smoothness properties of NeRF, Semantic-Human achieves consistent human parsing in both continuous and novel views. We also introduce constraints derived from the SMPL surface for the motion field and regularization for the recovered volumetric geometry. We have evaluated the model using the ZJU-MoCap dataset, and the obtained highly competitive results demonstrate the effectiveness of our proposed Semantic-Human. We also showcase various compelling applications, including label denoising, label synthesis and image editing, and empirically validate its advantageous properties.