Matías Mendieta

According to our database, Matías Mendieta authored at least 20 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Navigating Heterogeneity and Privacy in One-Shot Federated Learning with Diffusion Models.

Matías Mendieta, Guangyu Sun, Chen Chen

CoRR, 2024

Towards Multi-modal Transformers in Federated Learning.

Guangyu Sun, Matías Mendieta, Aritra Dutta, Xin Li, Chen Chen

Proceedings of the Computer Vision - ECCV 2024, 2024

2023

MutualNet: Adaptive ConvNet via Mutual Learning From Different Model Configurations.

Taojiannan Yang, Sijie Zhu, Matías Mendieta, Pu Wang, Ravikumar Balakrishnan, Minwoo Lee, Tao Han, Mubarak Shah, Chen Chen

IEEE Trans. Pattern Anal. Mach. Intell., 2023

GFM: Building Geospatial Foundation Models via Continual Pretraining.

Matías Mendieta, Boran Han, Xingjian Shi, Yi Zhu, Chen Chen, Mu Li

CoRR, 2023

POSTER: A Pyramid Cross-Fusion Transformer Network for Facial Expression Recognition.

Ce Zheng, Matías Mendieta, Chen Chen

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated Learning.

Guangyu Sun, Matías Mendieta, Jun Luo, Shandong Wu, Chen Chen

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Towards Geospatial Foundation Models via Continual Pretraining.

Matías Mendieta, Boran Han, Xingjian Shi, Yi Zhu, Chen Chen

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

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

Jun Luo, Matías Mendieta, Chen Chen, Shandong Wu

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

FeatER: An Efficient Network for Human Reconstruction via Feature Map-Based TransformER.

Ce Zheng, Matías Mendieta, Taojiannan Yang, Guo-Jun Qi, Chen Chen

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

Exploring Parameter-Efficient Fine-tuning for Improving Communication Efficiency in Federated Learning.

Guangyu Sun, Matías Mendieta, Taojiannan Yang, Chen Chen

CoRR, 2022

HeatER: An Efficient and Unified Network for Human Reconstruction via Heatmap-based TransformER.

Ce Zheng, Matías Mendieta, Taojiannan Yang, Chen Chen

CoRR, 2022

A Lightweight Graph Transformer Network for Human Mesh Reconstruction from 2D Human Pose.

Ce Zheng, Matías Mendieta, Pu Wang, Aidong Lu, Chen Chen

Proceedings of the MM '22: The 30th ACM International Conference on Multimedia, Lisboa, Portugal, October 10, 2022

Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning.

Matías Mendieta, Taojiannan Yang, Pu Wang, Minwoo Lee, Zhengming Ding, Chen Chen

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021

Mez: An Adaptive Messaging System for Latency-Sensitive Multi-Camera Machine Vision at the IoT Edge.

Anjus George, Arun Ravindran, Matías Mendieta, Hamed Tabkhi

IEEE Access, 2021

3D Human Pose Estimation with Spatial and Temporal Transformers.

Ce Zheng, Sijie Zhu, Matías Mendieta, Taojiannan Yang, Chen Chen, Zhengming Ding

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

CARPe Posterum: A Convolutional Approach for Real-Time Pedestrian Path Prediction.

Matías Mendieta, Hamed Tabkhi

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

REVAMP2T: Real-Time Edge Video Analytics for Multicamera Privacy-Aware Pedestrian Tracking.

Christopher Neff, Matías Mendieta, Shrey Mohan, Mohammadreza Baharani, Samuel Rogers, Hamed Tabkhi

IEEE Internet Things J., 2020

A3D: Adaptive 3D Networks for Video Action Recognition.

Sijie Zhu, Taojiannan Yang, Matías Mendieta, Chen Chen

CoRR, 2020

Mez: A Messaging System for Latency-Sensitive Multi-Camera Machine Vision at the IoT Edge.

Anjus George, Arun Ravindran, Matías Mendieta, Hamed Tabkhi

CoRR, 2020

2019

REVAMP²T: Real-time Edge Video Analytics for Multi-camera Privacy-aware Pedestrian Tracking.

Christopher Neff, Matías Mendieta, Shrey Mohan, Mohammadreza Baharani, Samuel Rogers, Hamed Tabkhi

CoRR, 2019