117 citations · 600 across the 38 of their papers we have counts for
7 papers · 1 filter
PPOM: Marginalizing Patch-Grid Phase for CLIP-Based Generalizable Vision-Language Prompt Tuning
Liang Wang, Haoyang Li, Chao Wang +3
Prompt tuning adapts CLIP-based vision-language models with few trainable parameters, yet its predictions remain sensitive to the spatial sampling imposed by a frozen vision transf…
Raw Data Matters: Enhancing Prompt Tuning by Internal Augmentation on Vision-Language Models
Haoyang Li, Liang Wang, Chao Wang +4
For CLIP-based prompt tuning, introducing more data as additional knowledge for enhancing fine-tuning process is proved to be an effective approach. Existing data amplification str…
DPC: Dual-Prompt Collaboration for Tuning Vision-Language Models
Haoyang Li, Liang Wang, Chao Wang +3
The Base-New Trade-off (BNT) problem universally exists during the optimization of CLIP-based prompt tuning, where continuous fine-tuning on base (target) classes leads to a simult…
Isometric Propagation Network for Generalized Zero-shot Learning
Lu Liu, Tianyi Zhou, Guodong Long +3
Zero-shot learning (ZSL) aims to classify images of an unseen class only based on a few attributes describing that class but no access to any training sample. A popular strategy is…
PICA: A Pixel Correlation-based Attentional Black-box Adversarial Attack
Jie Wang, Zhaoxia Yin, Jin Tang +2
The studies on black-box adversarial attacks have become increasingly prevalent due to the intractable acquisition of the structural knowledge of deep neural networks (DNNs). Howev…
Confusable Learning for Large-class Few-Shot Classification
Bingcong Li, Bo Han, Zhuowei Wang +2
Few-shot image classification is challenging due to the lack of ample samples in each class. Such a challenge becomes even tougher when the number of classes is very large, i.e., t…