Search Results for author:
Found 11 papers, 9 papers with code
FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated Learning
Personalized Federated Learning (PFL) represents a promising solution for decentralized learning in heterogeneous data environments.
Towards Geospatial Foundation Models via Continual Pretraining
Geospatial technologies are becoming increasingly essential in our world for a wide range of applications, including agriculture, urban planning, and disaster response.
PGFed: Personalize Each Client's Global Objective for Federated Learning
Based on our observation, in this work, we propose Personalized Global Federated Learning (PGFed), a novel personalized FL framework that enables each client to personalize its own global objective by explicitly and adaptively aggregating the empirical risks of itself and other clients.
Conquering the Communication Constraints to Enable Large Pre-Trained Models in Federated Learning
Recently, the use of small pre-trained models has been shown effective in federated learning optimization and improving convergence.
FeatER: An Efficient Network for Human Reconstruction via Feature Map-Based TransformER
Recently, vision transformers have shown great success in a set of human reconstruction tasks such as 2D human pose estimation (2D HPE), 3D human pose estimation (3D HPE), and human mesh reconstruction (HMR) tasks.
Ranked #30 on
3D Human Pose Estimation
on 3DPW
POSTER: A Pyramid Cross-Fusion Transformer Network for Facial Expression Recognition
In this paper, we propose a two-stream Pyramid crOss-fuSion TransformER network (POSTER), that aims to holistically solve all three issues.
Facial Expression Recognition
Facial Expression Recognition (FER)
Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning
To alleviate this issue, many FL algorithms focus on mitigating the effects of data heterogeneity across clients by introducing a variety of proximal terms, some incurring considerable compute and/or memory overheads, to restrain local updates with respect to the global model.
A Lightweight Graph Transformer Network for Human Mesh Reconstruction from 2D Human Pose
We propose a pose analysis module that uses graph transformers to exploit structured and implicit joint correlations, and a mesh regression module that combines the extracted pose feature with the mesh template to reconstruct the final human mesh.
Ranked #61 on
3D Human Pose Estimation
on 3DPW
MutualNet: Adaptive ConvNet via Mutual Learning from Different Model Configurations
MutualNet is a general training methodology that can be applied to various network structures (e. g., 2D networks: MobileNets, ResNet, 3D networks: SlowFast, X3D) and various tasks (e. g., image classification, object detection, segmentation, and action recognition), and is demonstrated to achieve consistent improvements on a variety of datasets.
3D Human Pose Estimation with Spatial and Temporal Transformers
Transformer architectures have become the model of choice in natural language processing and are now being introduced into computer vision tasks such as image classification, object detection, and semantic segmentation.
Ranked #13 on
Monocular 3D Human Pose Estimation
on Human3.6M
A3D: Adaptive 3D Networks for Video Action Recognition
Even under the same computational constraints, the performance of our adaptive networks can be significantly boosted over the baseline counterparts by the mutual training along three dimensions.
