23 citations · 61 across the 15 of their papers we have counts for
18 papers
Efficient Segment Anything with Depth-Aware Fusion and Limited Training Data
Yiming Zhou, Xuenjie Xie, Panfeng Li +3
Segment Anything Models (SAM) achieve impressive universal segmentation performance but require massive datasets (e.g., 11M images) and rely solely on RGB inputs. Recent efficient…
Differentially Private Perturbed Push-Sum Protocol and Its Application in Non-Convex Optimization
Yiming Zhou, Kaiping Xue, Enhong Chen
In decentralized networks, nodes cannot ensure that their shared information will be securely preserved by their neighbors, making privacy vulnerable to inference by curious nodes.…
PiXTime: A Model for Federated Time Series Forecasting with Heterogeneous Data across Nodes
Yiming Zhou, Jiahao Wang, Mingyue Cheng +3
While collaborative forecasting on distributed time series is highly desirable, directly pooling localized datasets is often impractical due to data sharing constraints. Federated…
FUSCO: High-Performance Distributed Data Shuffling via Transformation-Communication Fusion
Zhuoran Zhu, Chunyang Zhu, Hao Lin +9
Large-scale Mixture-of-Experts (MoE) models rely on \emph{expert parallelism} for efficient training and inference, which splits experts across devices and necessitates distributed…
Classifier-Centric Adaptive Framework for Open-Vocabulary Camouflaged Object Segmentation
Hanyu Zhang, Yiming Zhou, Jinxia Zhang
Open-vocabulary camouflaged object segmentation requires models to segment camouflaged objects of arbitrary categories unseen during training, placing extremely high demands on gen…
Scalable Hessian-free Proximal Conjugate Gradient Method for Nonconvex and Nonsmooth Optimization
Yiming Zhou, Wei Dai
This work studies a composite minimization problem involving a differentiable function q and a nonsmooth function h, both of which may be nonconvex. This problem is ubiquitous in s…
Adaptive Weighting Push-SUM for Decentralized Optimization with Statistical Diversity
Yiming Zhou, Yifei Cheng, Linli Xu +1
Statistical diversity is a property of data distribution and can hinder the optimization of a decentralized network. However, the theoretical limitations of the Push-SUM protocol r…
Evaluating Modern Approaches in 3D Scene Reconstruction: NeRF vs Gaussian-Based Methods
Yiming Zhou, Zixuan Zeng, Andi Chen +7
Exploring the capabilities of Neural Radiance Fields (NeRF) and Gaussian-based methods in the context of 3D scene reconstruction, this study contrasts these modern approaches with…
Mapping New Realities: Ground Truth Image Creation with Pix2Pix Image-to-Image Translation
Zhenglin Li, Bo Guan, Yuanzhou Wei +3
Generative Adversarial Networks (GANs) have significantly advanced image processing, with Pix2Pix being a notable framework for image-to-image translation. This paper explores a no…
Integrating AI in NDE: Techniques, Trends, and Further Directions
Eduardo Pérez, Cemil Emre Ardic, Ozan Çakıroğlu +9
The digital transformation is fundamentally changing our industries, affecting planning, execution as well as monitoring of production processes in a wide range of application fiel…
Proximal Dogleg Opportunistic Majorization for Nonconvex and Nonsmooth Optimization
Yiming Zhou, Wei Dai
We consider minimizing a function consisting of a quadratic term and a proximable term which is possibly nonconvex and nonsmooth. This problem is also known as scaled proximal oper…
Jointly Learning Selection Matrices For Transmitters, Receivers And Fourier Coefficients In Multichannel Imaging
Han Wang, Yiming Zhou, Eduardo Perez +1
Strategic subsampling has become a focal point due to its effectiveness in compressing data, particularly in the Full Matrix Capture (FMC) approach in ultrasonic imaging. This pape…
Communication-Efficient Distributed Learning with Local Immediate Error Compensation
Yifei Cheng, Li Shen, Linli Xu +6
Gradient compression with error compensation has attracted significant attention with the target of reducing the heavy communication overhead in distributed learning. However, exis…
A Multi-Source Data Fusion-based Semantic Segmentation Model for Relic Landslide Detection
Yiming Zhou, Yuexing Peng, Daqing Ge +2
As a natural disaster, landslide often brings tremendous losses to human lives, so it urgently demands reliable detection of landslide risks. When detecting relic landslides that p…
SimpleNet: A Simple Network for Image Anomaly Detection and Localization
Zhikang Liu, Yiming Zhou, Yuansheng Xu +1
We propose a simple and application-friendly network (called SimpleNet) for detecting and localizing anomalies. SimpleNet consists of four components: (1) a pre-trained Feature Ext…
Improving Levenberg-Marquardt Algorithm for Neural Networks
Omead Pooladzandi, Yiming Zhou
We explore the usage of the Levenberg-Marquardt (LM) algorithm for regression (non-linear least squares) and classification (generalized Gauss-Newton methods) tasks in neural netwo…
Accuracy and Resiliency of Analog Compute-in-Memory Inference Engines
Zhe Wan, Tianyi Wang, Yiming Zhou +2
Recently, analog compute-in-memory (CIM) architectures based on emerging analog non-volatile memory (NVM) technologies have been explored for deep neural networks (DNN) to improve…
Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search
Xin Li, Yiming Zhou, Zheng Pan +1
Achieving good speed and accuracy trade-off on a target platform is very important in deploying deep neural networks in real world scenarios. However, most existing automatic archi…