2 citations · 7 across the 23 of their papers we have counts for
6 papers · 1 filter
LoRA-FAIR: Federated LoRA Fine-Tuning with Aggregation and Initialization Refinement
Jieming Bian, Lei Wang, Letian Zhang +1
Foundation models (FMs) achieve strong performance across diverse tasks with task-specific fine-tuning, yet full parameter fine-tuning is often computationally prohibitive for larg…
Learning the Optimal Path and DNN Partition for Collaborative Edge Inference
Yin Huang, Letian Zhang, Jie Xu
Recent advancements in Deep Neural Networks (DNNs) have catalyzed the development of numerous intelligent mobile applications and services. However, they also introduce significant…
Benchmarking Multi-Image Understanding in Vision and Language Models: Perception, Knowledge, Reasoning, and Multi-Hop Reasoning
Bingchen Zhao, Yongshuo Zong, Letian Zhang +1
The advancement of large language models (LLMs) has significantly broadened the scope of applications in natural language processing, with multi-modal LLMs extending these capabili…
ImageNet3D: Towards General-Purpose Object-Level 3D Understanding
Wufei Ma, Guanning Zeng, Guofeng Zhang +5
A vision model with general-purpose object-level 3D understanding should be capable of inferring both 2D (e.g., class name and bounding box) and 3D information (e.g., 3D location a…
Pre-Trained Vision-Language Models as Partial Annotators
Qian-Wei Wang, Yuqiu Xie, Letian Zhang +2
Pre-trained vision-language models learn massive data to model unified representations of images and natural languages, which can be widely applied to downstream machine learning t…
Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domains
Lei Wang, Jieming Bian, Letian Zhang +2
Federated learning (FL) allows collaborative machine learning training without sharing private data. While most FL methods assume identical data domains across clients, real-world…