144 citations · 546 across the 24 of their papers we have counts for
25 papers
DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey
Yu Qiao, Phuong-Nam Tran, Ji Su Yoon +4
Reinforcement learning (RL)-based large language models (LLMs), such as ChatGPT, DeepSeek, and Grok-3, have attracted widespread attention for their remarkable capabilities in mult…
Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence
Yu Qiao, Apurba Adhikary, Huy Q. Le +3
Federated learning (FL) has gained significant attention for enabling decentralized training on edge networks without exposing raw data. However, FL models remain susceptible to ad…
Boosting Federated Domain Generalization: Understanding the Role of Advanced Pre-Trained Architectures
Avi Deb Raha, Apurba Adhikary, Mrityunjoy Gain +2
In this study, we explore the efficacy of advanced pre-trained architectures, such as Vision Transformers (ViT), ConvNeXt, and Swin Transformers in enhancing Federated Domain Gener…
A Complete Survey on LLM-based AI Chatbots
Sumit Kumar Dam, Choong Seon Hong, Yu Qiao +1
The past few decades have witnessed an upsurge in data, forming the foundation for data-hungry, learning-based AI technology. Conversational agents, often referred to as AI chatbot…
Logit Calibration and Feature Contrast for Robust Federated Learning on Non-IID Data
Yu Qiao, Chaoning Zhang, Apurba Adhikary +1
Federated learning (FL) is a privacy-preserving distributed framework for collaborative model training on devices in edge networks. However, challenges arise due to vulnerability t…
FedCCL: Federated Dual-Clustered Feature Contrast Under Domain Heterogeneity
Yu Qiao, Huy Q. Le, Mengchun Zhang +3
Federated learning (FL) facilitates a privacy-preserving neural network training paradigm through collaboration between edge clients and a central server. One significant challenge…
Towards Robust Federated Learning via Logits Calibration on Non-IID Data
Yu Qiao, Apurba Adhikary, Chaoning Zhang +1
Federated learning (FL) is a privacy-preserving distributed management framework based on collaborative model training of distributed devices in edge networks. However, recent stud…
Sora as a World Model? A Complete Survey on Text-to-Video Generation
Fachrina Dewi Puspitasari, Chaoning Zhang, Joseph Cho +13
The evolution of video generation from text, from animating MNIST to simulating the world with Sora, has progressed at a breakneck speed. Here, we systematically discuss how far te…
MobileSAMv2: Faster Segment Anything to Everything
Chaoning Zhang, Dongshen Han, Sheng Zheng +3
Segment anything model (SAM) addresses two practical yet challenging segmentation tasks: \textbf{segment anything (SegAny)}, which utilizes a certain point to predict the mask for…
Contrastive encoder pre-training-based clustered federated learning for heterogeneous data
Ye Lin Tun, Minh N. H. Nguyen, Chu Myaet Thwal +2
Federated learning (FL) is a promising approach that enables distributed clients to collaboratively train a global model while preserving their data privacy. However, FL often suff…
Understanding Segment Anything Model: SAM is Biased Towards Texture Rather than Shape
Chaoning Zhang, Yu Qiao, Shehbaz Tariq +5
In contrast to the human vision that mainly depends on the shape for recognizing the objects, deep image recognition models are widely known to be biased toward texture. Recently,…
Generative AI-driven Semantic Communication Framework for NextG Wireless Network
Avi Deb Raha, Md. Shirajum Munir, Apurba Adhikary +2
This work designs a novel semantic communication (SemCom) framework for the next-generation wireless network to tackle the challenges of unnecessary transmission of vast amounts th…
MST-compression: Compressing and Accelerating Binary Neural Networks with Minimum Spanning Tree
Quang Hieu Vo, Linh-Tam Tran, Sung-Ho Bae +2
Binary neural networks (BNNs) have been widely adopted to reduce the computational cost and memory storage on edge-computing devices by using one-bit representation for activations…
Boosting Federated Learning Convergence with Prototype Regularization
Yu Qiao, Huy Q. Le, Choong Seon Hong
As a distributed machine learning technique, federated learning (FL) requires clients to collaboratively train a shared model with an edge server without leaking their local data.…
Swin Transformer-Based Dynamic Semantic Communication for Multi-User with Different Computing Capacity
Loc X. Nguyen, Ye Lin Tun, Yan Kyaw Tun +4
Semantic communication has gained significant attention from researchers as a promising technique to replace conventional communication in the next generation of communication syst…
FedMEKT: Distillation-based Embedding Knowledge Transfer for Multimodal Federated Learning
Huy Q. Le, Minh N. H. Nguyen, Chu Myaet Thwal +3
Federated learning (FL) enables a decentralized machine learning paradigm for multiple clients to collaboratively train a generalized global model without sharing their private dat…
Faster Segment Anything: Towards Lightweight SAM for Mobile Applications
Chaoning Zhang, Dongshen Han, Yu Qiao +4
Segment Anything Model (SAM) has attracted significant attention due to its impressive zero-shot transfer performance and high versatility for numerous vision applications (like im…
Robustness of SAM: Segment Anything Under Corruptions and Beyond
Yu Qiao, Chaoning Zhang, Taegoo Kang +3
Segment anything model (SAM), as the name suggests, is claimed to be capable of cutting out any object and demonstrates impressive zero-shot transfer performance with the guidance…
A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering
Chaoning Zhang, Joseph Cho, Fachrina Dewi Puspitasari +11
The Segment Anything Model (SAM), developed by Meta AI Research, represents a significant breakthrough in computer vision, offering a robust framework for image and video segmentat…
Generative AI meets 3D: A Survey on Text-to-3D in AIGC Era
Chenghao Li, Chaoning Zhang, Joseph Cho +6
Generative AI has made significant progress in recent years, with text-guided content generation being the most practical as it facilitates interaction between human instructions a…
Segment Anything Model (SAM) Meets Glass: Mirror and Transparent Objects Cannot Be Easily Detected
Dongsheng Han, Chaoning Zhang, Yu Qiao +5
Meta AI Research has recently released SAM (Segment Anything Model) which is trained on a large segmentation dataset of over 1 billion masks. As a foundation model in the field of…
One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era
Chaoning Zhang, Chenshuang Zhang, Chenghao Li +13
OpenAI has recently released GPT-4 (a.k.a. ChatGPT plus), which is demonstrated to be one small step for generative AI (GAI), but one giant leap for artificial general intelligence…
MP-FedCL: Multiprototype Federated Contrastive Learning for Edge Intelligence
Yu Qiao, Md. Shirajum Munir, Apurba Adhikary +4
Federated learning-assisted edge intelligence enables privacy protection in modern intelligent services. However, not independent and identically distributed (non-IID) distribution…
Prototype Helps Federated Learning: Towards Faster Convergence
Yu Qiao, Seong-Bae Park, Sun Moo Kang +1
Federated learning (FL) is a distributed machine learning technique in which multiple clients cooperate to train a shared model without exchanging their raw data. However, heteroge…