60 citations · 105 across the 16 of their papers we have counts for
16 papers
Navigating Heterogeneity and Privacy in One-Shot Federated Learning with Diffusion Models
Matias Mendieta, Guangyu Sun, Chen Chen
Federated learning (FL) enables multiple clients to train models collectively while preserving data privacy. However, FL faces challenges in terms of communication cost and data he…
Towards Multi-modal Transformers in Federated Learning
Guangyu Sun, Matias Mendieta, Aritra Dutta +2
Multi-modal transformers mark significant progress in different domains, but siloed high-quality data hinders their further improvement. To remedy this, federated learning (FL) has…
FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated Learning
Guangyu Sun, Matias Mendieta, Jun Luo +2
Personalized Federated Learning (PFL) represents a promising solution for decentralized learning in heterogeneous data environments. Partial model personalization has been proposed…
Towards Geospatial Foundation Models via Continual Pretraining
Matias Mendieta, Boran Han, Xingjian Shi +2
Geospatial technologies are becoming increasingly essential in our world for a wide range of applications, including agriculture, urban planning, and disaster response. To help imp…
PGFed: Personalize Each Client's Global Objective for Federated Learning
Jun Luo, Matias Mendieta, Chen Chen +1
Personalized federated learning has received an upsurge of attention due to the mediocre performance of conventional federated learning (FL) over heterogeneous data. Unlike convent…
Exploring Parameter-Efficient Fine-Tuning to Enable Foundation Models in Federated Learning
Guangyu Sun, Umar Khalid, Matias Mendieta +2
Federated learning (FL) has emerged as a promising paradigm for enabling the collaborative training of models without centralized access to the raw data on local devices. In the ty…
FeatER: An Efficient Network for Human Reconstruction via Feature Map-Based TransformER
Ce Zheng, Matias Mendieta, Taojiannan Yang +2
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 huma…
POSTER: A Pyramid Cross-Fusion Transformer Network for Facial Expression Recognition
Ce Zheng, Matias Mendieta, Chen Chen
Facial expression recognition (FER) is an important task in computer vision, having practical applications in areas such as human-computer interaction, education, healthcare, and o…
Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning
Matias Mendieta, Taojiannan Yang, Pu Wang +3
Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed learning with a network of clients (i.e., edge devices). However, the data distributi…
A Lightweight Graph Transformer Network for Human Mesh Reconstruction from 2D Human Pose
Ce Zheng, Matias Mendieta, Pu Wang +2
Existing deep learning-based human mesh reconstruction approaches have a tendency to build larger networks in order to achieve higher accuracy. Computational complexity and model s…
MutualNet: Adaptive ConvNet via Mutual Learning from Different Model Configurations
Taojiannan Yang, Sijie Zhu, Matias Mendieta +6
Most existing deep neural networks are static, which means they can only do inference at a fixed complexity. But the resource budget can vary substantially across different devices…
3D Human Pose Estimation with Spatial and Temporal Transformers
Ce Zheng, Sijie Zhu, Matias Mendieta +3
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, objec…
A3D: Adaptive 3D Networks for Video Action Recognition
Sijie Zhu, Taojiannan Yang, Matias Mendieta +1
This paper presents A3D, an adaptive 3D network that can infer at a wide range of computational constraints with one-time training. Instead of training multiple models in a grid-se…
Mez: A Messaging System for Latency-Sensitive Multi-Camera Machine Vision at the IoT Edge
Anjus George, Arun Ravindran, Mattias Mendieta +1
Mez is a publish-subscribe messaging system for latency sensitive multi-camera machine vision at the IoT Edge. Unlike existing messaging systems, Mez allows applications to specify…
CARPe Posterum: A Convolutional Approach for Real-time Pedestrian Path Prediction
Matías Mendieta, Hamed Tabkhi
Pedestrian path prediction is an essential topic in computer vision and video understanding. Having insight into the movement of pedestrians is crucial for ensuring safe operation…
REVAMPT: Real-time Edge Video Analytics for Multi-camera Privacy-aware Pedestrian Tracking
Christopher Neff, Matías Mendieta, Shrey Mohan +3
This article presents REVAMPT, Real-time Edge Video Analytics for Multi-camera Privacy-aware Pedestrian Tracking, as an integrated end-to-end IoT system for privacy-built-in de…