115 citations · 188 across the 13 of their papers we have counts for
13 papers
FastPano3D: Feed-Forward Indoor Panoramic 3D Reconstruction from a Single Image
Jianqiang Li, Liumei Zhang, Wenjia Guo +5
Recent advances in 3D scene reconstruction have highlighted the intricate trade-offs among rendering quality, inference efficiency, and data dependency. To address the challenge of…
From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling
Yi Hu, Hanchi Ren, Jingjing Deng +1
Stock price prediction is a critical area of financial forecasting, traditionally approached by training models using the historical price data of individual stocks. While these mo…
Distributed Learning for UAV Swarms
Chen Hu, Hanchi Ren, Jingjing Deng +1
Unmanned Aerial Vehicle (UAV) swarms are increasingly deployed in dynamic, data-rich environments for applications such as environmental monitoring and surveillance. These scenario…
Gradients Stand-in for Defending Deep Leakage in Federated Learning
H. Yi, H. Ren, C. Hu +3
Federated Learning (FL) has become a cornerstone of privacy protection, shifting the paradigm towards localizing sensitive data while only sending model gradients to a central serv…
Triplane Grasping: Efficient 6-DoF Grasping with Single RGB Images
Yiming Li, Hanchi Ren, Yue Yang +2
Reliable object grasping is one of the fundamental tasks in robotics. However, determining grasping pose based on single-image input has long been a challenge due to limited visual…
Task-oriented Robotic Manipulation with Vision Language Models
Nurhan Bulus Guran, Hanchi Ren, Jingjing Deng +1
Vision Language Models (VLMs) play a crucial role in robotic manipulation by enabling robots to understand and interpret the visual properties of objects and their surroundings, al…
FissionVAE: Federated Non-IID Image Generation with Latent Space and Decoder Decomposition
Chen Hu, Hanchi Ren, Jingjing Deng +2
Federated learning is a machine learning paradigm that enables decentralized clients to collaboratively learn a shared model while keeping all the training data local. While consid…
An Element-Wise Weights Aggregation Method for Federated Learning
Yi Hu, Hanchi Ren, Chen Hu +2
Federated learning (FL) is a powerful Machine Learning (ML) paradigm that enables distributed clients to collaboratively learn a shared global model while keeping the data on the o…
A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspective
Xianghua Xie, Chen Hu, Hanchi Ren +1
This review paper takes a comprehensive look at malicious attacks against FL, categorizing them from new perspectives on attack origins and targets, and providing insights into the…
Gradient Leakage Defense with Key-Lock Module for Federated Learning
Hanchi Ren, Jingjing Deng, Xianghua Xie
Federated Learning (FL) is a widely adopted privacy-preserving machine learning approach where private data remains local, enabling secure computations and the exchange of local mo…
GRNN: Generative Regression Neural Network -- A Data Leakage Attack for Federated Learning
Hanchi Ren, Jingjing Deng, Xianghua Xie
Data privacy has become an increasingly important issue in Machine Learning (ML), where many approaches have been developed to tackle this challenge, e.g. cryptography (Homomorphic…
FedBoosting: Federated Learning with Gradient Protected Boosting for Text Recognition
Hanchi Ren, Jingjing Deng, Xianghua Xie +2
Typical machine learning approaches require centralized data for model training, which may not be possible where restrictions on data sharing are in place due to, for instance, pri…
From Pose to Activity: Surveying Datasets and Introducing CONVERSE
Michael Edwards, Jingjing Deng, Xianghua Xie
We present a review on the current state of publicly available datasets within the human action recognition community; highlighting the revival of pose based methods and recent pro…