Adaptive cross-modal alignment via symmetric prompt tuning for few-shot vision–language learning
Open Access
- 9 July 2026
- journal article
- research article
- Published by Springer Nature in Journal of King Saud University Computer and Information Sciences
- Vol. 38 (6) , 594
Abstract
No abstract availableKeywords
Funding Information
- Fundamental Research Funds for the Central Universities (2682026CX04)
- Fundamental Research Funds for the Central Universities (2682025ZTPY052, 2682023ZDPY001)
- National Natural Science Foundation of China (2024NSFC0658)
- Sichuan Science and Technology Program (2024NSFC0658)
This publication has 49 references indexed in Scilit:
- CRAD-HOPE: brain-inspired nested learning framework for few-shot anomaly detectionJournal of King Saud University Computer and Information Sciences, 2026
- A few-shot learning approach based on Brownian distance covariance for plant disease classificationJournal of King Saud University Computer and Information Sciences, 2025
- Advanced deep learning and large language models: Comprehensive insights for cancer detectionImage and Vision Computing, 2025
- Enhancing few-shot image classification through learnable multi-scale embedding and attention mechanismsNeural Networks, 2025
- CGCN: Context graph convolutional network for few-shot temporal action localizationInformation Processing & Management, 2024
- MED-Prompt: A novel prompt engineering framework for medicine prediction on free-text clinical notesJournal of King Saud University Computer and Information Sciences, 2024
- Tip-Adapter: Training-Free Adaption of CLIP for Few-Shot Classification2022
- Learning to Prompt for Vision-Language ModelsInternational Journal of Computer Vision, 2022
- Rethinking Few-Shot Image Classification: A Good Embedding is All You Need?2020
- Food-101 – Mining Discriminative Components with Random Forests2014