9 papers
Guided Prompt Evolution for Vision-Language Models Adaptation
Enming Zhang, Jiayang Li, Yanlong Wang +3
The adaptation of large-scale vision-language models (VLMs) to downstream tasks with limited labeled data remains a significant challenge. While parameter-efficient prompt learning…
Unified Optimization of Source Weights and Transfer Quantities in Multi-Source Transfer Learning: An Asymptotic Framework
Qingyue Zhang, Chang Chu, Haohao Fu +5
In multi-source transfer learning, a key challenge lies in how to appropriately differentiate and utilize heterogeneous source tasks. However, existing multi-source methods typical…
Understanding Knowledge Transferability for Transfer Learning: A Survey
Haohua Wang, Jingge Wang, Zijie Zhao +9
Transfer learning has become an essential paradigm in artificial intelligence, enabling the transfer of knowledge from a source task to improve performance on a target task. This a…
Learning Optimal Prompt Ensemble for Multi-source Visual Prompt Transfer
Enming Zhang, Liwen Cao, Yanru Wu +2
Prompt tuning has emerged as a lightweight strategy for adapting foundation models to downstream tasks, particularly for resource-constrained systems. As pre-trained prompts become…
TMT: Cross-domain Semantic Segmentation with Region-adaptive Transferability Estimation
Enming Zhang, Zhengyu Li, Yanru Wu +5
Recent advances in Vision Transformers (ViTs) have significantly advanced semantic segmentation performance. However, their adaptation to new target domains remains challenged by d…
Exploiting Task Relationships in Continual Learning via Transferability-Aware Task Embeddings
Yanru Wu, Jianning Wang, Xiangyu Chen +4
Continual learning (CL) has been a critical topic in contemporary deep neural network applications, where higher levels of both forward and backward transfer are desirable for an e…
A High-Dimensional Statistical Method for Optimizing Transfer Quantities in Multi-Source Transfer Learning
Qingyue Zhang, Haohao Fu, Guanbo Huang +7
Multi-source transfer learning provides an effective solution to data scarcity in real-world supervised learning scenarios by leveraging multiple source tasks. In this field, exist…
pFedGPA: Diffusion-based Generative Parameter Aggregation for Personalized Federated Learning
Jiahao Lai, Jiaqi Li, Jian Xu +6
Federated Learning (FL) offers a decentralized approach to model training, where data remains local and only model parameters are shared between the clients and the central server.…
H-ensemble: An Information Theoretic Approach to Reliable Few-Shot Multi-Source-Free Transfer
Yanru Wu, Jianning Wang, Weida Wang +1
Multi-source transfer learning is an effective solution to data scarcity by utilizing multiple source tasks for the learning of the target task. However, access to source data and…