no code implementations • 21 Jul 2024 • Yunyi Xuan, WeiJie Chen, Shicai Yang, Di Xie, Luojun Lin, Yueting Zhuang
In this paper, we discuss the extension of DFKD to Vision-Language Foundation Models without access to the billion-level image-text datasets.
1 code implementation • 20 Jun 2024 • Zhaozhe Hu, Jia-Li Yin, Bin Chen, Luojun Lin, Bo-Hao Chen, Ximeng Liu
Self-ensemble adversarial training methods improve model robustness by ensembling models at different training epochs, such as model weight averaging (WA).
1 code implementation • 23 Nov 2023 • Luojun Lin, Zhifeng Shen, Zhishu Sun, Yuanlong Yu, Lei Zhang, WeiJie Chen
The parameters of dynamic networks can be decoupled into a static and a dynamic component, which are designed to learn domain-invariant and domain-specific features, respectively.
1 code implementation • ICCV 2023 • Qipeng Liu, Luojun Lin, Zhifeng Shen, Zhifeng Yang
To address this issue, we propose the Periodically Exchange Teacher-Student (PETS) method, a simple yet novel approach that introduces a multiple-teacher framework consisting of a static teacher, a dynamic teacher, and a student model.
1 code implementation • 23 Nov 2023 • Luojun Lin, Zhifeng Shen, Jia-Li Yin, Qipeng Liu, Yuanlong Yu, WeiJie Chen
To this end, we propose a novel MetaFBP framework, in which we devise a universal feature extractor to capture the aesthetic commonality and then optimize to adapt the aesthetic individuality by shifting the decision boundary of the predictor via a meta-learning mechanism.
no code implementations • 25 Oct 2023 • WeiJie Chen, Haoyu Wang, Shicai Yang, Lei Zhang, Wei Wei, Yanning Zhang, Luojun Lin, Di Xie, Yueting Zhuang
Such a one-for-all adaptation paradigm allows us to adapt anything in the world using only one text-to-image generator as well as the corresponding unlabeled target data.
no code implementations • ICCV 2023 • Weizhen He, WeiJie Chen, Binbin Chen, Shicai Yang, Di Xie, Luojun Lin, Donglian Qi, Yueting Zhuang
In this paper, we delve into this problem and propose an Unsupervised Prompt Tuning framework for text-driven object detection, which is composed of two novel mean teaching mechanisms.
1 code implementation • CVPR 2022 • Rang Meng, WeiJie Chen, Shicai Yang, Jie Song, Luojun Lin, Di Xie, ShiLiang Pu, Xinchao Wang, Mingli Song, Yueting Zhuang
In this paper, we introduce a simple framework, Slimmable Domain Adaptation, to improve cross-domain generalization with a weight-sharing model bank, from which models of different capacities can be sampled to accommodate different accuracy-efficiency trade-offs.
1 code implementation • 27 May 2022 • Zhishu Sun, Zhifeng Shen, Luojun Lin, Yuanlong Yu, Zhifeng Yang, Shicai Yang, WeiJie Chen
Specifically, we leverage a meta-adjuster to twist the network parameters based on the static model with respect to different data from different domains.
Ranked #26 on
Domain Generalization
on DomainNet
1 code implementation • 19 Nov 2021 • Luojun Lin, Han Xie, Zhishu Sun, WeiJie Chen, Wenxi Liu, Yuanlong Yu, Lei Zhang
From this perspective, we introduce a novel paradigm of DG, termed as Semi-Supervised Domain Generalization (SSDG), to explore how the labeled and unlabeled source domains can interact, and establish two settings, including the close-set and open-set SSDG.
no code implementations • 23 Feb 2021 • WeiJie Chen, Luojun Lin, Shicai Yang, Di Xie, ShiLiang Pu, Yueting Zhuang, Wenqi Ren
Usually, the given source domain pre-trained model is expected to optimize with only unlabeled target data, which is termed as source-free unsupervised domain adaptation.
1 code implementation • 20 Jun 2020 • Wei-Jie Chen, ShiLiang Pu, Di Xie, Shicai Yang, Yilu Guo, Luojun Lin
Extensive experiments on ImageNet dataset have been conducted to prove the effectiveness of our method.
no code implementations • 13 Nov 2019 • Songxuan Lai, Lianwen Jin, Luojun Lin, Yecheng Zhu, Huiyun Mao
To tackle this issue, this paper proposes to learn dynamic signature representations through ranking synthesized signatures.
5 code implementations • 19 Jan 2018 • Lingyu Liang, Luojun Lin, Lianwen Jin, Duorui Xie, Mengru Li
Previous works have formulated the recognition of facial beauty as a specific supervised learning problem of classification, regression or ranking, which indicates that FBP is intrinsically a computation problem with multiple paradigms.
Ranked #2 on
Facial Beauty Prediction
on SCUT-FBP