6 papers
SPJFNet: Self-Mining Prior-Guided Joint Frequency Enhancement for Ultra-Efficient Dark Image Restoration
Tongshun Zhang, Pingling Liu, Zijian Zhang +1
Current dark image restoration methods suffer from severe efficiency bottlenecks, primarily stemming from: (1) computational burden and error correction costs associated with relia…
The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions
Ping Liu, Jiawei Du
Dataset distillation, which condenses large-scale datasets into compact synthetic representations, has emerged as a critical solution for training modern deep learning models effic…
Fed-pilot: Optimizing LoRA Allocation for Efficient Federated Fine-Tuning with Heterogeneous Clients
Zikai Zhang, Rui Hu, Ping Liu +1
Federated Learning enables the fine-tuning of foundation models (FMs) across distributed clients for specific tasks; however, its scalability is limited by the heterogeneity of cli…
Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation
Zikai Zhang, Ping Liu, Jiahao Xu +1
Federated Learning has recently been utilized to collaboratively fine-tune foundation models across multiple clients. Notably, federated low-rank adaptation LoRA-based fine-tuning…
Erasing Concepts, Steering Generations: A Comprehensive Survey of Concept Suppression
Yiwei Xie, Ping Liu, Zheng Zhang
Text-to-Image (T2I) models have demonstrated impressive capabilities in generating high-quality and diverse visual content from natural language prompts. However, uncontrolled repr…
CLIP-SR: Collaborative Linguistic and Image Processing for Super-Resolution
Bingwen Hu, Heng Liu, Zhedong Zheng +1
Convolutional Neural Networks (CNNs) have significantly advanced Image Super-Resolution (SR), yet most CNN-based methods rely solely on pixel-based transformations, often leading t…