4 citations · 12 across the 12 of their papers we have counts for
4 papers · 1 filter
Cross-Level Distillation and Feature Denoising for Cross-Domain Few-Shot Classification
Hao Zheng, Runqi Wang, Jianzhuang Liu +1
The conventional few-shot classification aims at learning a model on a large labeled base dataset and rapidly adapting to a target dataset that is from the same distribution as the…
Self-Enhancement Improves Text-Image Retrieval in Foundation Visual-Language Models
Yuguang Yang, Yiming Wang, Shupeng Geng +4
The emergence of cross-modal foundation models has introduced numerous approaches grounded in text-image retrieval. However, on some domain-specific retrieval tasks, these models f…
Few-Shot Learning with Visual Distribution Calibration and Cross-Modal Distribution Alignment
Runqi Wang, Hao Zheng, Xiaoyue Duan +5
Pre-trained vision-language models have inspired much research on few-shot learning. However, with only a few training images, there exist two crucial problems: (1) the visual feat…
AttriCLIP: A Non-Incremental Learner for Incremental Knowledge Learning
Runqi Wang, Xiaoyue Duan, Guoliang Kang +5
Continual learning aims to enable a model to incrementally learn knowledge from sequentially arrived data. Previous works adopt the conventional classification architecture, which…