5 papers · 1 filter
Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs
Zixuan Hu, Yongxian Wei, Li Shen +2
Large Language Models (LLMs) such as ChatGPT demonstrate strong few-shot adaptability without requiring fine-tuning, positioning them ideal for data-limited and real-time applicati…
GSRender: Deduplicated Occupancy Prediction via Weakly Supervised 3D Gaussian Splatting
Qianpu Sun, Changyong Shu, Sifan Zhou +6
Weakly-supervised 3D occupancy perception is crucial for vision-based autonomous driving in outdoor environments. Previous methods based on NeRF often face a challenge in balancing…
Learn To Learn More Precisely
Runxi Cheng, Yongxian Wei, Xianglong He +5
Meta-learning has been extensively applied in the domains of few-shot learning and fast adaptation, achieving remarkable performance. While Meta-learning methods like Model-Agnosti…
Task Groupings Regularization: Data-Free Meta-Learning with Heterogeneous Pre-trained Models
Yongxian Wei, Zixuan Hu, Li Shen +4
Data-Free Meta-Learning (DFML) aims to derive knowledge from a collection of pre-trained models without accessing their original data, enabling the rapid adaptation to new unseen t…
FREE: Faster and Better Data-Free Meta-Learning
Yongxian Wei, Zixuan Hu, Zhenyi Wang +3
Data-Free Meta-Learning (DFML) aims to extract knowledge from a collection of pre-trained models without requiring the original data, presenting practical benefits in contexts cons…