5 papers
CF-LoRA: Decoupled Factor Aggregation and Adaptation-Aware Client Clustering for Federated LoRA Fine-Tuning
Mengjun Yi, Langxing Yang, Suhan Guo +2
Federated LoRA fine-tuning enables parameter-efficient adaptation of pre-trained models without sharing private data, but suffers from two fundamental mismatches under heterogeneou…
CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models
Mengjun Yi, Hanwen Zhang, Hui Dou +2
Large pre-trained Vision-Language Models (VLMs), such as Contrastive Language-Image Pre-training (CLIP), have exhibited remarkable zero-shot performance across various image classi…
SPAT: Sensitivity-based Multihead-attention Pruning on Time Series Forecasting Models
Suhan Guo, Jiahong Deng, Mengjun Yi +2
Attention-based architectures have achieved superior performance in multivariate time series forecasting but are computationally expensive. Techniques such as patching and adaptive…
Interactive Instance Annotation with Siamese Networks
Xiang Xu, Ruotong Li, Mengjun Yi +3
Annotating instance masks is time-consuming and labor-intensive. A promising solution is to predict contours using a deep learning model and then allow users to refine them. Howeve…
Explaining Model Overfitting in CNNs via GMM Clustering
Hui Dou, Xinyu Mu, Mengjun Yi +3
Convolutional Neural Networks (CNNs) have demonstrated remarkable prowess in the field of computer vision. However, their opaque decision-making processes pose significant challeng…