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学者姓名：林洛君
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Abstract ：
We study a novel problem in this paper, that is, if a modern text-to-image diffusion model can tailor any image classifier across domains and categories. Existing domain adaption works exploit both source and target data for domain alignment so as to transfer the knowledge from the labeled source data to the unlabeled target data. However, as the development of text-to-image diffusion models, we wonder if the high-fidelity synthetic data can serve as a surrogate of the source data in real world. In this way, we do not need to collect and annotate the source data for each image classification task in a one-for-one manner. Instead, we utilize only one off-the-shelf text-to-image model to synthesize images with labels derived from text prompts, and then leverage them as a bridge to dig out the knowledge from the task-agnostic text-to-image generator to the task-oriented image classifier via domain adaptation. 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 any unlabeled target data. Extensive experiments validate the feasibility of this idea, which even surprisingly surpasses the state-of-the-art domain adaptation works using the source data collected and annotated in real world.
Keyword ：
data synthesis data synthesis prompt diversification prompt diversification Text-to-image diffusion models Text-to-image diffusion models unsupervised domain adaptation unsupervised domain adaptation
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| GB/T 7714 | Chen, Weijie , Wang, Haoyu , Yang, Shicai et al. Adapt Anything: Tailor Any Image Classifier Across Domains and Categories Using Text-to-Image Diffusion Models [J]. | IEEE TRANSACTIONS ON BIG DATA , 2025 , 11 (3) : 1013-1026 . |
| MLA | Chen, Weijie et al. "Adapt Anything: Tailor Any Image Classifier Across Domains and Categories Using Text-to-Image Diffusion Models" . | IEEE TRANSACTIONS ON BIG DATA 11 . 3 (2025) : 1013-1026 . |
| APA | Chen, Weijie , Wang, Haoyu , Yang, Shicai , Zhang, Lei , Wei, Wei , Zhang, Yanning et al. Adapt Anything: Tailor Any Image Classifier Across Domains and Categories Using Text-to-Image Diffusion Models . | IEEE TRANSACTIONS ON BIG DATA , 2025 , 11 (3) , 1013-1026 . |
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Facial beauty prediction (FBP) aims to develop a system to assess facial attractiveness automatically. Through prior research and our own observations, it has become evident that attribute information, such as gender and race, is a key factor leading to the distribution discrepancy in the FBP data. Such distribution discrepancy hinders current conventional FBP models from generalizing effectively to unseen attribute domain data, thereby discounting further performance improvement. To address this problem, in this paper, we exploit the attribute information to guide the training of convolutional neural networks (CNNs), with the final purpose of implicit feature alignment across various attribute domain data. To this end, we introduce the attribute information into convolution layer and batch normalization (BN) layer, respectively, as they are the most crucial parts for representation learning in CNNs. Specifically, our method includes: 1) Attribute -guided convolution (AgConv) that dynamically updates convolutional filters based on attributes by parameter tuning or parameter rebirth; 2) Attribute -guided batch normalization (AgBN) is developed to compute the attribute -specific statistics through an attribute guided batch sampling strategy; 3) To benefit from both approaches, we construct an integrated framework by combining AgConv and AgBN to achieve a more thorough feature alignment across different attribute domains. Extensive qualitative and quantitative experiments have been conducted on the SCUTFBP, SCUT-FBP5500 and HotOrNot benchmark datasets. The results show that AgConv significantly improves the attribute -guided representation learning capacity and AgBN provides more stable optimization. Owing to the combination of AgConv and AgBN, the proposed framework (Ag-Net) achieves further performance improvement and is superior to other state-of-the-art approaches for FBP.
Keyword ：
Batch normalization Batch normalization Dynamic convolution Dynamic convolution Facial attractiveness assessment Facial attractiveness assessment Facial beauty prediction Facial beauty prediction
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| GB/T 7714 | Sun, Zhishu , Lin, Luojun , Yu, Yuanlong et al. Learning feature alignment across attribute domains for improving facial beauty prediction [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 249 . |
| MLA | Sun, Zhishu et al. "Learning feature alignment across attribute domains for improving facial beauty prediction" . | EXPERT SYSTEMS WITH APPLICATIONS 249 (2024) . |
| APA | Sun, Zhishu , Lin, Luojun , Yu, Yuanlong , Jin, Lianwen . Learning feature alignment across attribute domains for improving facial beauty prediction . | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 249 . |
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Abstract ：
Facial Beauty Prediction (FBP) is a significant pattern recognition task that aims to achieve consistent facial attractiveness assessment with human perception. Currently, Convolutional Neural Networks (CNNs) have become the mainstream method for FBP. The training objective of most conventional CNNs is usually to learn static convolution kernels, which, however, makes the network quite difficult to capture global attentive information, and thus usually ignores the key facial regions, e.g., eyes, and nose. To tackle this problem, we devise a new convolution manner, Dynamic Attentive Convolution (DyAttenConv), which integrates the dynamic and attention mechanism into convolution in kernel -level, with the aim of enforcing the convolution kernels adapted to each face dynamically. DyAttenConv is a plug -and -play module that can be flexibly combined with existing CNN architectures, making the acquisition of the beauty -related features more globally and attentively. Extensive ablation studies show that our method is superior to other fusion and attention mechanisms, and the comparison with other state -of -the -arts also demonstrates the effectiveness of DyAttenConv on facial beauty prediction task.
Keyword ：
dynamic convolution dynamic convolution facial beauty prediction facial beauty prediction kernel attention kernel attention
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| GB/T 7714 | Sun, Zhishu , Xiao, Zilong , Yu, Yuanlong et al. Dynamic Attentive Convolution for Facial Beauty Prediction [J]. | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS , 2024 , E107 (2) : 239-243 . |
| MLA | Sun, Zhishu et al. "Dynamic Attentive Convolution for Facial Beauty Prediction" . | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS E107 . 2 (2024) : 239-243 . |
| APA | Sun, Zhishu , Xiao, Zilong , Yu, Yuanlong , Lin, Luojun . Dynamic Attentive Convolution for Facial Beauty Prediction . | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS , 2024 , E107 (2) , 239-243 . |
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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 can be cumbersome or even infeasible to obtain in practice due to the vast amount of data involved. Web data, namely web -crawled images, offers an opportunity to access large amounts of unlabeled images with rich style information, which can be leveraged to improve DG. 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. The close -set SSDG is based on existing public DG datasets, while the open -set SSDG, built on the newly -collected web -crawled datasets, presents a novel yet realistic challenge that pushes the limits of current technologies. A natural approach of SSDG is to transfer knowledge from labeled data to unlabeled data via pseudo labeling, and train the model on both labeled and pseudo -labeled data for generalization. Since there are conflicting goals between domain -oriented pseudo labeling and out -of -domain generalization, we develop a pseudo labeling phase and a generalization phase independently for SSDG. Unfortunately, due to the large domain gap, the pseudo labels provided in the pseudo labeling phase inevitably contain noise, which has negative affect on the subsequent generalization phase. Therefore, to improve the quality of pseudo labels and further enhance generalizability, we propose a cyclic learning framework to encourage a positive feedback between these two phases, utilizing an evolving intermediate domain that bridges the labeled and unlabeled domains in a curriculum learning manner. Extensive experiments are conducted to validate the effectiveness of our method. It is worth highlighting that web -crawled images can promote domain generalization as demonstrated by the experimental results.
Keyword ：
Domain generalization Domain generalization Semi-supervised learning Semi-supervised learning Transfer learning Transfer learning Unsupervised domain adaptation Unsupervised domain adaptation
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| GB/T 7714 | Lin, Luojun , Xie, Han , Sun, Zhishu et al. Semi-supervised domain generalization with evolving intermediate domain [J]. | PATTERN RECOGNITION , 2024 , 149 . |
| MLA | Lin, Luojun et al. "Semi-supervised domain generalization with evolving intermediate domain" . | PATTERN RECOGNITION 149 (2024) . |
| APA | Lin, Luojun , Xie, Han , Sun, Zhishu , Chen, Weijie , Liu, Wenxi , Yu, Yuanlong et al. Semi-supervised domain generalization with evolving intermediate domain . | PATTERN RECOGNITION , 2024 , 149 . |
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Unsupervised Domain Adaptive Object Detection (DAOD) task can relax the domain shift problem between source and target domains, which requires to train models on labeled source and unlabeled target domains jointly. However, due to limitations of data privacy protection, the source domain data is usually inaccessible, which poses significant challenges for the DAOD task. Hence, Source-Free Object Detection (SFOD) task has been developed that aims to fine-tune a pre-trained source model with only unlabeled target domain data. Most of the existing SFOD methods are based on pseudo labeling using the student-teacher framework, where the teacher model is the Exponential Moving Average (EMA) of the student models in different time steps. However, these methods always exist a knowledge bias problem due to class imbalance, and therefore, a fixed EMA update rate is no longer suitable for different classes. For high-quality classes, a fast EMA rate can accelerate knowledge updating and promote model convergence, while for low-quality classes, a fast EMA rate can accelerate the accumulation of knowledge bias and lead to the collapse of such categories. To solve this problem, we propose a novel SFOD method called Slow-Fast Adaptation which develops two different teacher models, a slow teacher, and a fast teacher model, to jointly guide the student training. The slow and fast teacher models can provide richer supervision information and complement each other. The experiments on four benchmark datasets show that our method achieves state-of-the-art results and even outperforms DAOD methods in some cases, which demonstrate the effectiveness of our method on the SFOD task. © 2024 IEEE.
Keyword ：
Data privacy Data privacy Differential privacy Differential privacy Domain Knowledge Domain Knowledge Personnel training Personnel training Problem solving Problem solving Students Students Teaching Teaching
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| GB/T 7714 | Lin, Luojun , Liu, Qipeng , Zheng, Xiangwei et al. Slow-Fast Adaptation for Source-Free Object Detection [C] . 2024 . |
| MLA | Lin, Luojun et al. "Slow-Fast Adaptation for Source-Free Object Detection" . (2024) . |
| APA | Lin, Luojun , Liu, Qipeng , Zheng, Xiangwei , Lin, Zheng . Slow-Fast Adaptation for Source-Free Object Detection . (2024) . |
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Image segmentation tasks aim to separate the image into masks that represent different objects or regions, where deep-learning-based methods have become mainstream. In the common practice, researchers utilize large-scale datasets including images along with their annotations to train their models, and evaluate the predictions with evaluation metrics. However, to our knowledge, no metrics have been proposed to assess the quality of the segmentation annotations, which will bring benefits to both the labeling and experimental process. In this paper, we fill this research gap and propose the first no-reference segmentation annotation quality assessment named SAQ. Based on our observation, we utilize the normal gradients of pixels on the annotation contours to represent the degree of fitting the real contours, which reflect the annotation accuracy. To alleviate the image differences, we adopt the gradient ranking score rather than directly using the gradient value. The multi-scale strategy is introduced to accommodate annotations of objects with different structures. Extensive experiments on datasets for various segmentation tasks have demonstrated the rationality of our proposed SAQ, and the assessment results of their annotation quality can serve as significant references for researchers. © 2024 IEEE.
Keyword ：
Deep learning Deep learning Image annotation Image annotation Image segmentation Image segmentation
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| GB/T 7714 | Lin, Zheng , Duan, Zheng-Peng , Zhang, Xuying et al. No-Reference Segmentation Annotation Quality Assessment [C] . 2024 . |
| MLA | Lin, Zheng et al. "No-Reference Segmentation Annotation Quality Assessment" . (2024) . |
| APA | Lin, Zheng , Duan, Zheng-Peng , Zhang, Xuying , Lin, Luojun . No-Reference Segmentation Annotation Quality Assessment . (2024) . |
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With the growing significance of data privacy protection, Source-Free Domain Adaptation (SFDA) has gained attention as a research topic that aims to transfer knowledge from a labeled source domain to an unlabeled target domain without accessing source data. However, the absence of source data often leads to model collapse or restricts the performance improvements of SFDA methods, as there is insufficient true-labeled knowledge for each category. To tackle this, Source-Free Active Domain Adaptation (SFADA) has emerged as a new task that aims to improve SFDA by selecting a small set of informative target samples labeled by experts. Nevertheless, existing SFADA methods impose a significant burden on human labelers, requiring them to continuously label a substantial number of samples throughout the training period. In this paper, a novel approach is proposed to alleviate the labeling burden in SFADA by only necessitating the labeling of an extremely small number of samples on a one-time basis. Moreover, considering the inherent sparsity of these selected samples in the target domain, a Self-adaptive Clustering-based Active Learning (SCAL) method is proposed that propagates the labels of selected samples to other datapoints within the same cluster. To further enhance the accuracy of SCAL, a self-adaptive scale search method is devised that automatically determines the optimal clustering scale, using the entropy of the entire target dataset as a guiding criterion. The experimental evaluation presents compelling evidence of our method's supremacy. Specifically, it outstrips previous SFDA methods, delivering state-of-the-art (SOTA) results on standard benchmarks. Remarkably, it accomplishes this with less than 0.5% annotation cost, in stark contrast to the approximate 5% required by earlier techniques. The approach thus not only sets new performance benchmarks but also offers a markedly more practical and cost-effective solution for SFADA, making it an attractive choice for real-world applications where labeling resources are limited. We propose a novel approach to alleviate the labeling burden in SFADA by only necessitating the labeling of an extremely small number of samples on a one-time basis. Moreover, considering the inherent sparsity of these selected samples in the target domain, we propose a Self-adaptive Clustering-based Active Learning (SCAL) method that propagates the labels of selected samples to other datapoints within the same cluster. To further enhance the accuracy of SCAL, we devise an self-adaptive scale search method that automatically determines the optimal clustering scale, using the entropy of the entire target dataset as a guiding criterion.image
Keyword ：
computer vision computer vision image recognition image recognition
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| GB/T 7714 | Sun, Zhishu , Lin, Luojun , Yu, Yuanlong . You only label once: A self-adaptive clustering-based method for source-free active domain adaptation [J]. | IET IMAGE PROCESSING , 2024 , 18 (5) : 1268-1282 . |
| MLA | Sun, Zhishu et al. "You only label once: A self-adaptive clustering-based method for source-free active domain adaptation" . | IET IMAGE PROCESSING 18 . 5 (2024) : 1268-1282 . |
| APA | Sun, Zhishu , Lin, Luojun , Yu, Yuanlong . You only label once: A self-adaptive clustering-based method for source-free active domain adaptation . | IET IMAGE PROCESSING , 2024 , 18 (5) , 1268-1282 . |
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Recently, there has been increasing interest in the Source-Free Domain Adaptive Object Detection task, which involves training an object detector on the unlabeled target data using a pre-trained source model without accessing the source data. Most related methods are developed from the mean-teacher framework, which aims to train the student model closer to the teacher model via a pseudo labeling manner, where the teacher model is the exponential-moving-average of the student models at different time-steps. Following this line of works, we propose a Run-and-Chase Mutual-Learning method to strengthen the interactions between the student model and the teacher model in both feature and prediction levels. In our method, the student model is optimized to run away from the teacher model at the feature level, while chasing the teacher model at the prediction level. In this way, the student model is forced to be distinguishable at different time-steps, so that the teacher model can acquire more diverse task-related information and produce higher-accuracy pseudo labels. As the training goes, the student and teacher models are updated iteratively and promoted mutually, which can prevent the model collapse problem. Extensive experiments are conducted to validate the effectiveness of our method.
Keyword ：
Object Detection Object Detection Transfer Learning Transfer Learning Unsupervised Domain Adaptation Unsupervised Domain Adaptation
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| GB/T 7714 | Lin, Luojun , Yang, Zhifeng , Liu, Qipeng et al. Run and Chase: Towards Accurate Source-Free Domain Adaptive Object Detection [J]. | 2023 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, ICME , 2023 : 2453-2458 . |
| MLA | Lin, Luojun et al. "Run and Chase: Towards Accurate Source-Free Domain Adaptive Object Detection" . | 2023 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, ICME (2023) : 2453-2458 . |
| APA | Lin, Luojun , Yang, Zhifeng , Liu, Qipeng , Yu, Yuanlong , Lin, Qifeng . Run and Chase: Towards Accurate Source-Free Domain Adaptive Object Detection . | 2023 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, ICME , 2023 , 2453-2458 . |
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Data-Free Knowledge Distillation (DFKD) aims to craft a customized student model from a pre-trained teacher model by synthesizing surrogate training images. However, a seldom-investigated scenario is to distill the knowledge to multiple heterogeneous students simultaneously. In this paper, we aim to study how to improve the performance by coevolving peer students, termed Data-Free Multi-Student Coevolved Distillation (DF-MSCD). Based on previous DFKD methods, we advance DF-MSCD by improving the data quality from the perspective of synthesizing unbiased, informative and diverse surrogate samples: 1) Unbiased. The disconnection of image synthesis among different timestamps during DFKD will lead to an unnoticed class imbalance problem. To tackle this problem, we reform the prior art into an unbiased variant by bridging the label distribution of the synthesized data among different timestamps. 2) Informative. Different from single-student DFKD, we encourage the interactions not only between teacher-student pairs, but also within peer students, driving a more comprehensive knowledge distillation. To this end, we devise a novel Inter-Student Adversarial Learning method to coevolve peer students with mutual benefits. 3) Diverse. To further promote Inter-Student Adversarial Learning, we develop Mixture-of-Generators, in which multiple generators are optimized to synthesize different yet complementary samples by playing min-max games with multiple students. Experiments are conducted to validate the effectiveness and efficiency of the proposed DF-MSCD, surpassing the existing state-of-the-arts on multiple popular benchmarks. To emphasize, our method can obtain heterogeneous students by training once, which is superior to single-student DFKD methods in terms of both training time and testing accuracy.
Keyword ：
Adversarial training Adversarial training Knowledge distillation Knowledge distillation Model inversion Model inversion Mutual learning Mutual learning Surrogate images Surrogate images
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| GB/T 7714 | Chen, Weijie , Xuan, Yunyi , Yang, Shicai et al. Better Together: Data-Free Multi-Student Coevolved Distillation [J]. | KNOWLEDGE-BASED SYSTEMS , 2023 , 283 . |
| MLA | Chen, Weijie et al. "Better Together: Data-Free Multi-Student Coevolved Distillation" . | KNOWLEDGE-BASED SYSTEMS 283 (2023) . |
| APA | Chen, Weijie , Xuan, Yunyi , Yang, Shicai , Xie, Di , Lin, Luojun , Zhuang, Yueting . Better Together: Data-Free Multi-Student Coevolved Distillation . | KNOWLEDGE-BASED SYSTEMS , 2023 , 283 . |
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In the age of social media, posting attractive mugshots is commonplace, leading to an urgent need for automatic facial beautification techniques. To better meet the esthetic preferences of users, we devise a customized automatic face beautification task that can retouch the face adaptively to match the user-entered target score whilst preserving the ID information as much as possible. To accomplish this task, we propose a Human Esthetics Guided StyleGAN Inversion method to retouch each face in the embedding space using StyleGAN inversion. This process is guided by a pre-trained facial beauty prediction model that measures the difference between the target score and the predicted score of the retouched face. We conduct extensive experiments on various faces with different attributes, where the experimental results show that our method achieves the competitive performance, both in terms of visual effect and the proposed criterion. © 2023 IEEE.
Keyword ：
Computer vision Computer vision
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| GB/T 7714 | Chen, Wang , Chen, Peizhen , Chen, Weijie et al. Customized Automatic Face Beautification [C] . 2023 . |
| MLA | Chen, Wang et al. "Customized Automatic Face Beautification" . (2023) . |
| APA | Chen, Wang , Chen, Peizhen , Chen, Weijie , Lin, Luojun . Customized Automatic Face Beautification . (2023) . |
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