314 citations · 341 across the 25 of their papers we have counts for
27 papers
Imagine then Verify: Affordance-Targeted Active Perception for Task-Oriented Grasping in Cluttered Scenes
Jingzhi Cui, Xuefeng Liu, Feng Han +3
Task-oriented grasping (TOG) requires robots to grasp functional parts of objects (e.g., the handle of a mug for pouring), yet these affordance regions are frequently occluded in c…
DL-SLAM: Enabling High-Fidelity Gaussian Splatting SLAM in Dynamic Environments based on Dual-Level Probability
Ziheng Xu, Qingfeng Li, Xuefeng Liu +2
Recent advances in 3D Gaussian Splatting (3DGS) have enabled significant progress in dense dynamic Simultaneous Localization And Mapping (SLAM). Prevailing methods typically discar…
From Coordinate Matching to Structural Alignment: Rethinking Prototype Alignment in Heterogeneous Federated Learning
Xinghao Wu, Jianwei Niu, Guogang Zhu +3
Heterogeneous federated learning (HtFL) aims to enable collaboration among clients that differ in both data distributions and model architectures. Prototype-based methods, which co…
Learning to Optimize Job Shop Scheduling Under Structural Uncertainty
Rui Zhang, Jianwei Niu, Xuefeng Liu +2
The Job-Shop Scheduling Problem (JSSP), under various forms of manufacturing uncertainty, has recently attracted considerable research attention. Most existing studies focus on par…
Can Federated Learning Safeguard Private Data in LLM Training? Vulnerabilities, Attacks, and Defense Evaluation
Wenkai Guo, Xuefeng Liu, Haolin Wang +3
Fine-tuning large language models (LLMs) with local data is a widely adopted approach for organizations seeking to adapt LLMs to their specific domains. Given the shared characteri…
Enhancing Visual Representation with Textual Semantics: Textual Semantics-Powered Prototypes for Heterogeneous Federated Learning
Xinghao Wu, Jianwei Niu, Xuefeng Liu +4
Federated Prototype Learning (FedPL) has emerged as an effective strategy for handling data heterogeneity in Federated Learning (FL). In FedPL, clients collaboratively construct a…
The Other Side of the Coin: Unveiling the Downsides of Model Aggregation in Federated Learning from a Layer-peeled Perspective
Guogang Zhu, Xuefeng Liu, Jianwei Niu +2
It is often observed that the aggregated model in FL underperforms on local data until after several rounds of local training. This temporary performance drop can potentially slow…
Why Go Full? Elevating Federated Learning Through Partial Network Updates
Haolin Wang, Xuefeng Liu, Jianwei Niu +2
Federated learning is a distributed machine learning paradigm designed to protect user data privacy, which has been successfully implemented across various scenarios. In traditiona…
GLC-SLAM: Gaussian Splatting SLAM with Efficient Loop Closure
Ziheng Xu, Qingfeng Li, Chen Chen +2
3D Gaussian Splatting (3DGS) has gained significant attention for its application in dense Simultaneous Localization and Mapping (SLAM), enabling real-time rendering and high-fidel…
Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation
Xinghao Wu, Jianwei Niu, Xuefeng Liu +4
Federated Learning (FL) faces challenges due to data heterogeneity, which limits the global model's performance across diverse client distributions. Personalized Federated Learning…
The Diversity Bonus: Learning from Dissimilar Distributed Clients in Personalized Federated Learning
Xinghao Wu, Xuefeng Liu, Jianwei Niu +4
Personalized Federated Learning (PFL) is a commonly used framework that allows clients to collaboratively train their personalized models. PFL is particularly useful for handling s…
DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations
Guogang Zhu, Xuefeng Liu, Jianwei Niu +3
In personalized federated learning (PFL), it is widely recognized that achieving both high model generalization and effective personalization poses a significant challenge due to t…
Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank Decomposition
Xinghao Wu, Xuefeng Liu, Jianwei Niu +4
To address data heterogeneity, the key strategy of Personalized Federated Learning (PFL) is to decouple general knowledge (shared among clients) and client-specific knowledge, as t…
Estimating before Debiasing: A Bayesian Approach to Detaching Prior Bias in Federated Semi-Supervised Learning
Guogang Zhu, Xuefeng Liu, Xinghao Wu +4
Federated Semi-Supervised Learning (FSSL) leverages both labeled and unlabeled data on clients to collaboratively train a model.In FSSL, the heterogeneous data can introduce predic…
Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration
Xinghao Wu, Xuefeng Liu, Jianwei Niu +2
Personalized federated learning (PFL) reduces the impact of non-independent and identically distributed (non-IID) data among clients by allowing each client to train a personalized…
Take Your Pick: Enabling Effective Personalized Federated Learning within Low-dimensional Feature Space
Guogang Zhu, Xuefeng Liu, Shaojie Tang +3
Personalized federated learning (PFL) is a popular framework that allows clients to have different models to address application scenarios where clients' data are in different doma…
3Deformer: A Common Framework for Image-Guided Mesh Deformation
Hao Su, Xuefeng Liu, Jianwei Niu +2
We propose 3Deformer, a general-purpose framework for interactive 3D shape editing. Given a source 3D mesh with semantic materials, and a user-specified semantic image, 3Deformer c…
Unlocking the Potential of Federated Learning for Deeper Models
Haolin Wang, Xuefeng Liu, Jianwei Niu +2
Federated learning (FL) is a new paradigm for distributed machine learning that allows a global model to be trained across multiple clients without compromising their privacy. Alth…
OpenVIS: Open-vocabulary Video Instance Segmentation
Pinxue Guo, Tony Huang, Peiyang He +4
Open-vocabulary Video Instance Segmentation (OpenVIS) can simultaneously detect, segment, and track arbitrary object categories in a video, without being constrained to categories…
MARVEL: Raster Manga Vectorization via Primitive-wise Deep Reinforcement Learning
Hao Su, Jianwei Niu, Xuefeng Liu +2
Manga is a fashionable Japanese-style comic form that is composed of black-and-white strokes and is generally displayed as raster images on digital devices. Typical mangas have sim…
Visformer: The Vision-friendly Transformer
Zhengsu Chen, Lingxi Xie, Jianwei Niu +3
The past year has witnessed the rapid development of applying the Transformer module to vision problems. While some researchers have demonstrated that Transformer-based models enjo…
SelectScale: Mining More Patterns from Images via Selective and Soft Dropout
Zhengsu Chen, Jianwei Niu, Xuefeng Liu +1
Convolutional neural networks (CNNs) have achieved remarkable success in image recognition. Although the internal patterns of the input images are effectively learned by the CNNs,…
An End-to-end Method for Producing Scanning-robust Stylized QR Codes
Hao Su, Jianwei Niu, Xuefeng Liu +4
Quick Response (QR) code is one of the most worldwide used two-dimensional codes.~Traditional QR codes appear as random collections of black-and-white modules that lack visual sema…
Network Adjustment: Channel Search Guided by FLOPs Utilization Ratio
Zhengsu Chen, Jianwei Niu, Lingxi Xie +3
Automatic designing computationally efficient neural networks has received much attention in recent years. Existing approaches either utilize network pruning or leverage the networ…