8 citations · 19 across the 8 of their papers we have counts for
4 papers · 1 filter
NLPrompt: Noise-Label Prompt Learning for Vision-Language Models
Bikang Pan, Qun Li, Xiaoying Tang +6
The emergence of vision-language foundation models, such as CLIP, has revolutionized image-text representation, enabling a broad range of applications via prompt learning. Despite…
Exclusive Style Removal for Cross Domain Novel Class Discovery
Yicheng Wang, Feng Liu, Junmin Liu +1
As a promising field in open-world learning, \textit{Novel Class Discovery} (NCD) is usually a task to cluster unseen novel classes in an unlabeled set based on the prior knowledge…
Negative Label Guided OOD Detection with Pretrained Vision-Language Models
Xue Jiang, Feng Liu, Zhen Fang +4
Out-of-distribution (OOD) detection aims at identifying samples from unknown classes, playing a crucial role in trustworthy models against errors on unexpected inputs. Extensive re…
Partition Speeds Up Learning Implicit Neural Representations Based on Exponential-Increase Hypothesis
Ke Liu, Feng Liu, Haishuai Wang +3
(INRs) aim to learn a (i.e., a neural network) to represent an image, where the input and output of the fu…