Search Results for author: Luojun Lin

Found 12 papers, 7 papers with code

MetaFBP: Learning to Learn High-Order Predictor for Personalized Facial Beauty Prediction

1 code implementation23 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.

Facial Beauty Prediction Meta-Learning

Periodically Exchange Teacher-Student for Source-Free Object Detection

no code implementations 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.

object-detection Object Detection

Parameter Exchange for Robust Dynamic Domain Generalization

1 code implementation23 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.

Disentanglement Domain Generalization

Adapt Anything: Tailor Any Image Classifiers across Domains And Categories Using Text-to-Image Diffusion Models

no code implementations25 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.

Domain Adaptation Image Classification

Unsupervised Prompt Tuning for Text-Driven Object Detection

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.

Data Augmentation Object +4

Slimmable Domain Adaptation

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.

Domain Generalization Unsupervised Domain Adaptation

Dynamic Domain Generalization

1 code implementation27 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.

Domain Generalization

Semi-Supervised Domain Generalization with Evolving Intermediate Domain

1 code implementation19 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.

Domain Generalization Semi-Supervised Domain Generalization

Self-Supervised Noisy Label Learning for Source-Free Unsupervised Domain Adaptation

no code implementations23 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.

Self-Supervised Learning Unsupervised Domain Adaptation

Unsupervised Image Classification for Deep Representation Learning

1 code implementation20 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.

Classification Clustering +13

SynSig2Vec: Learning Representations from Synthetic Dynamic Signatures for Real-world Verification

no code implementations13 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.

Representation Learning

SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction

5 code implementations19 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.

Facial Beauty Prediction General Classification +1

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