5 papers · 1 filter
Representation Calibration and Uncertainty Guidance for Class-Incremental Learning based on Vision Language Model
Jiantao Tan, Peixian Ma, Tong Yu +2
Class-incremental learning requires a learning system to continually learn knowledge of new classes and meanwhile try to preserve previously learned knowledge of old classes. As cu…
Preserve and Sculpt: Manifold-Aligned Fine-tuning of Vision-Language Models for Few-Shot Learning
Dexia Chen, Qianjie Zhu, Weibing Li +3
Pretrained vision-language models (VLMs), such as CLIP, have shown remarkable potential in few-shot image classification and led to numerous effective transfer learning strategies.…
Cross-Domain Few-Shot Learning via Multi-View Collaborative Optimization with Vision-Language Models
Dexia Chen, Wentao Zhang, Qianjie Zhu +4
Vision-language models (VLMs) pre-trained on natural image and language data, such as CLIP, have exhibited significant potential in few-shot image recognition tasks, leading to dev…
Decoupling Continual Semantic Segmentation
Yifu Guo, Yuquan Lu, Wentao Zhang +5
Continual Semantic Segmentation (CSS) requires learning new classes without forgetting previously acquired knowledge, addressing the fundamental challenge of catastrophic forgettin…
Intensive Vision-guided Network for Radiology Report Generation
Fudan Zheng, Mengfei Li, Ying Wang +5
Automatic radiology report generation is booming due to its huge application potential for the healthcare industry. However, existing computer vision and natural language processin…