4 papers
Concept-wise Attention for Fine-grained Concept Bottleneck Models
Minghong Zhong, Guoshuai Zou, Kanghao Chen +2
Recently impressive performance has been achieved in Concept Bottleneck Models (CBM) by utilizing the image-text alignment learned by a large pre-trained vision-language model (i.e…
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…
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…