3 papers
cs.AI2025
Large model retrieval enhancement framework for construction site risk identification
Jiawei Li, Chengye Yang, Yaochen Zhang +3
This study addresses construction site hazard identification by proposing a retrieval-augmented framework that enhances large language models (LLMs) without requiring fine-tuning.…
cs.CV2025
ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification
Kexuan Shi, Zhuang Qi, Jingjing Zhu +4
Open-set few-shot image classification aims to train models using a small amount of labeled data, enabling them to achieve good generalization when confronted with unknown environm…
cs.CV2025
LLM-Enabled Style and Content Regularization for Personalized Text-to-Image Generation
Anran Yu, Wei Feng, Yaochen Zhang +4
The personalized text-to-image generation has rapidly advanced with the emergence of Stable Diffusion. Existing methods, which typically fine-tune models using embedded identifiers…