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cs.CV2025
E2MPL:An Enduring and Efficient Meta Prompt Learning Framework for Few-shot Unsupervised Domain Adaptation
Wanqi Yang, Haoran Wang, Lei Wang +3
Few-shot unsupervised domain adaptation (FS-UDA) leverages a limited amount of labeled data from a source domain to enable accurate classification in an unlabeled target domain. De…
cs.CV2024
Hybrid Feature Collaborative Reconstruction Network for Few-Shot Fine-Grained Image Classification
Shulei Qiu, Wanqi Yang, Ming Yang
Our research focuses on few-shot fine-grained image classification, which faces two major challenges: appearance similarity of fine-grained objects and limited number of samples. T…