68 citations · 105 across the 19 of their papers we have counts for
12 papers · 1 filter
Concrete Subspace Learning based Interference Elimination for Multi-task Model Fusion
Anke Tang, Xianglin Luo, Li Shen +5
Merging models fine-tuned from a common, extensively pre-trained large model but specialized for different tasks has been demonstrated as a cheap and scalable strategy to construct…
Federated Learning with Manifold Regularization and Normalized Update Reaggregation
Xuming An, Li Shen, Han Hu +1
Federated Learning (FL) is an emerging collaborative machine learning framework where multiple clients train the global model without sharing their own datasets. In FL, the model i…
Learning from models beyond fine-tuning
Hongling Zheng, Li Shen, Anke Tang +5
Foundation models (FM) have demonstrated remarkable performance across a wide range of tasks (especially in the fields of natural language processing and computer vision), primaril…
Parameter Efficient Multi-task Model Fusion with Partial Linearization
Anke Tang, Li Shen, Yong Luo +5
Large pre-trained models have enabled significant advances in machine learning and served as foundation components. Model fusion methods, such as task arithmetic, have been proven…
DeViT: Decomposing Vision Transformers for Collaborative Inference in Edge Devices
Guanyu Xu, Zhiwei Hao, Yong Luo +3
Recent years have witnessed the great success of vision transformer (ViT), which has achieved state-of-the-art performance on multiple computer vision benchmarks. However, ViT mode…
PartSeg: Few-shot Part Segmentation via Part-aware Prompt Learning
Mengya Han, Heliang Zheng, Chaoyue Wang +4
In this work, we address the task of few-shot part segmentation, which aims to segment the different parts of an unseen object using very few labeled examples. It is found that lev…
Rethinking the Localization in Weakly Supervised Object Localization
Rui Xu, Yong Luo, Han Hu +3
Weakly supervised object localization (WSOL) is one of the most popular and challenging tasks in computer vision. This task is to localize the objects in the images given only the…
LGViT: Dynamic Early Exiting for Accelerating Vision Transformer
Guanyu Xu, Jiawei Hao, Li Shen +4
Recently, the efficient deployment and acceleration of powerful vision transformers (ViTs) on resource-limited edge devices for providing multimedia services have become attractive…
Multi-Granularity Hand Action Detection
Ting Zhe, Jing Zhang, Yongqian Li +3
Detecting hand actions in videos is crucial for understanding video content and has diverse real-world applications. Existing approaches often focus on whole-body actions or coarse…
Improving Heterogeneous Model Reuse by Density Estimation
Anke Tang, Yong Luo, Han Hu +5
This paper studies multiparty learning, aiming to learn a model using the private data of different participants. Model reuse is a promising solution for multiparty learning, assum…
SGDA: Towards 3D Universal Pulmonary Nodule Detection via Slice Grouped Domain Attention
Rui Xu, Zhi Liu, Yong Luo +5
Lung cancer is the leading cause of cancer death worldwide. The best solution for lung cancer is to diagnose the pulmonary nodules in the early stage, which is usually accomplished…
FedABC: Targeting Fair Competition in Personalized Federated Learning
Dui Wang, Li Shen, Yong Luo +4
Federated learning aims to collaboratively train models without accessing their client's local private data. The data may be Non-IID for different clients and thus resulting in poo…