6 citations · 6 across the 4 of their papers we have counts for
5 papers · 1 filter
Fine-Grained VLM Fine-tuning via Latent Hierarchical Adapter Learning
Yumiao Zhao, Bo Jiang, Yuhe Ding +3
Adapter-based approaches have garnered attention for fine-tuning pre-trained Vision-Language Models (VLMs) on few-shot classification tasks. These methods strive to develop a light…
Harmonizing and Merging Source Models for CLIP-based Domain Generalization
Yuhe Ding, Jian Liang, Bo Jiang +3
CLIP-based domain generalization aims to improve model generalization to unseen domains by leveraging the powerful zero-shot classification capabilities of CLIP and multiple source…
Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks
Yuhe Ding, Bo Jiang, Aihua Zheng +2
Vision language models (VLMs) like CLIP show stellar zero-shot capability on classification benchmarks. However, selecting the VLM with the highest performance on the unlabeled dow…
ProxyMix: Proxy-based Mixup Training with Label Refinery for Source-Free Domain Adaptation
Yuhe Ding, Lijun Sheng, Jian Liang +2
Unsupervised domain adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain. Owing to privacy concerns and heavy data transmission, s…
Unsupervised Contrastive Photo-to-Caricature Translation based on Auto-distortion
Yuhe Ding, Xin Ma, Mandi Luo +2
Photo-to-caricature translation aims to synthesize the caricature as a rendered image exaggerating the features through sketching, pencil strokes, or other artistic drawings. Style…