3 citations · 8 across the 10 of their papers we have counts for
6 papers · 1 filter
Enhancing Zero-Shot Vision Models by Label-Free Prompt Distribution Learning and Bias Correcting
Xingyu Zhu, Beier Zhu, Yi Tan +3
Vision-language models, such as CLIP, have shown impressive generalization capacities when using appropriate text descriptions. While optimizing prompts on downstream labeled data…
Selective Vision-Language Subspace Projection for Few-shot CLIP
Xingyu Zhu, Beier Zhu, Yi Tan +3
Vision-language models such as CLIP are capable of mapping the different modality data into a unified feature space, enabling zero/few-shot inference by measuring the similarity of…
Rethinking Visual Content Refinement in Low-Shot CLIP Adaptation
Jinda Lu, Shuo Wang, Yanbin Hao +3
Recent adaptations can boost the low-shot capability of Contrastive Vision-Language Pre-training (CLIP) by effectively facilitating knowledge transfer. However, these adaptation me…
Model Inversion Attacks Through Target-Specific Conditional Diffusion Models
Ouxiang Li, Yanbin Hao, Zhicai Wang +4
Model inversion attacks (MIAs) aim to reconstruct private images from a target classifier's training set, thereby raising privacy concerns in AI applications. Previous GAN-based MI…
Hierarchical Space-Time Attention for Micro-Expression Recognition
Haihong Hao, Shuo Wang, Huixia Ben +3
Micro-expression recognition (MER) aims to recognize the short and subtle facial movements from the Micro-expression (ME) video clips, which reveal real emotions. Recent MER method…
Boosting Few-Shot Learning via Attentive Feature Regularization
Xingyu Zhu, Shuo Wang, Jinda Lu +3
Few-shot learning (FSL) based on manifold regularization aims to improve the recognition capacity of novel objects with limited training samples by mixing two samples from differen…