26 citations · 47 across the 10 of their papers we have counts for
11 papers
Distilling Vision-Language Foundation Models: A Data-Free Approach via Prompt Diversification
Yunyi Xuan, Weijie Chen, Shicai Yang +3
Data-Free Knowledge Distillation (DFKD) has shown great potential in creating a compact student model while alleviating the dependency on real training data by synthesizing surroga…
MetaFBP: Learning to Learn High-Order Predictor for Personalized Facial Beauty Prediction
Luojun Lin, Zhifeng Shen, Jia-Li Yin +3
Predicting individual aesthetic preferences holds significant practical applications and academic implications for human society. However, existing studies mainly focus on learning…
Parameter Exchange for Robust Dynamic Domain Generalization
Luojun Lin, Zhifeng Shen, Zhishu Sun +3
Agnostic domain shift is the main reason of model degradation on the unknown target domains, which brings an urgent need to develop Domain Generalization (DG). Recent advances at D…
Adapt Anything: Tailor Any Image Classifiers across Domains And Categories Using Text-to-Image Diffusion Models
Weijie Chen, Haoyu Wang, Shicai Yang +6
We do not pursue a novel method in this paper, but aim to study if a modern text-to-image diffusion model can tailor any task-adaptive image classifier across domains and categorie…
Slimmable Domain Adaptation
Rang Meng, Weijie Chen, Shicai Yang +7
Vanilla unsupervised domain adaptation methods tend to optimize the model with fixed neural architecture, which is not very practical in real-world scenarios since the target data…
Dynamic Domain Generalization
Zhishu Sun, Zhifeng Shen, Luojun Lin +4
Domain generalization (DG) is a fundamental yet very challenging research topic in machine learning. The existing arts mainly focus on learning domain-invariant features with limit…
Semi-Supervised Domain Generalization with Evolving Intermediate Domain
Luojun Lin, Han Xie, Zhishu Sun +4
Domain Generalization (DG) aims to generalize a model trained on multiple source domains to an unseen target domain. The source domains always require precise annotations, which ca…
Self-Supervised Noisy Label Learning for Source-Free Unsupervised Domain Adaptation
Weijie Chen, Luojun Lin, Shicai Yang +4
It is a strong prerequisite to access source data freely in many existing unsupervised domain adaptation approaches. However, source data is agnostic in many practical scenarios du…
Unsupervised Image Classification for Deep Representation Learning
Weijie Chen, Shiliang Pu, Di Xie +3
Deep clustering against self-supervised learning is a very important and promising direction for unsupervised visual representation learning since it requires little domain knowled…
SynSig2Vec: Learning Representations from Synthetic Dynamic Signatures for Real-world Verification
Songxuan Lai, Lianwen Jin, Luojun Lin +2
An open research problem in automatic signature verification is the skilled forgery attacks. However, the skilled forgeries are very difficult to acquire for representation learnin…
SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction
Lingyu Liang, Luojun Lin, Lianwen Jin +2
Facial beauty prediction (FBP) is a significant visual recognition problem to make assessment of facial attractiveness that is consistent to human perception. To tackle this proble…