Query：
学者姓名：林洛君
Refining：
Year
Type
Indexed by
Source
Complex
Former Name
Co-
Language
Clean All
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 等. "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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Due to the high joint flexibility and deformation degree of hands, hand pose estimation is more challenging in the detection task. In order to ensure the accuracy of prediction, two-stage algorithms are proposed recently, which requires a huge and redundant model structure and is difficult to implement end-to-end deployment. In this paper, we propose a novel dynamic single-stage CNN (RetinaHand) for end-to-end 2D handpose estimation of RGB images based on RetinaNet. RetinaHand firstly extracts image features through the backbone with dynamic convolutional layers. In the neck module, we propose Context Path Aggregation Network (CPANet) that fuse different scale features and expands context information to improve performance. In addition, we use the idea of multi-task learning to add a keypoints heatmap regression branch on the basis of the existing classification and bounding box regression branch, and use multi-task loss training model. Experimental results on the Eric.Lee and Panoptic datasets consistently show that our proposed RetinaHand has comparable performance to existing hand pose estimation methods at more efficient inference rates.
Keyword ：
Hand pose estimation Hand pose estimation Multi-task learning Multi-task learning
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Xiao, Zilong , Lin, Luojun , Yang, Yuanxi et al. RetinaHand: Towards Accurate Single-Stage Hand Pose Estimation [J]. | ADVANCES IN NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, ICNC-FSKD 2022 , 2023 , 153 : 639-647 . |
| MLA | Xiao, Zilong et al. "RetinaHand: Towards Accurate Single-Stage Hand Pose Estimation" . | ADVANCES IN NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, ICNC-FSKD 2022 153 (2023) : 639-647 . |
| APA | Xiao, Zilong , Lin, Luojun , Yang, Yuanxi , Yu, Yuanlong . RetinaHand: Towards Accurate Single-Stage Hand Pose Estimation . | ADVANCES IN NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, ICNC-FSKD 2022 , 2023 , 153 , 639-647 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Grounded language-image pre-trained models have shown strong zero-shot generalization to various downstream object detection tasks. Despite their promising performance, the models rely heavily on the laborious prompt engineering. Existing works typically address this problem by tuning text prompts using downstream training data in a few-shot or fully supervised manner. However, a rarely studied problem is to optimize text prompts without using any annotations. 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. In conventional mean teaching, the quality of pseudo boxes is expected to optimize better as the training goes on, but there is still a risk of overfitting noisy pseudo boxes. To mitigate this problem, 1) we propose Nested Mean Teaching, which adopts nested-annotation to supervise teacher-student mutual learning in a bi-level optimization manner; 2) we propose Dual Complementary Teaching, which employs an offline pre-trained teacher and an online mean teacher via data-augmentation-based complementary labeling so as to ensure learning without accumulating confirmation bias. By integrating these two mechanisms, the proposed unsupervised prompt tuning framework achieves significant performance improvement on extensive object detection datasets.
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | He, Weizhen , Chen, Weijie , Chen, Binbin et al. Unsupervised Prompt Tuning for Text-Driven Object Detection [J]. | CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION, ICCV , 2023 : 2651-2661 . |
| MLA | He, Weizhen et al. "Unsupervised Prompt Tuning for Text-Driven Object Detection" . | CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION, ICCV (2023) : 2651-2661 . |
| APA | He, Weizhen , Chen, Weijie , Chen, Binbin , Yang, Shicai , Xie, Di , Lin, Luojun et al. Unsupervised Prompt Tuning for Text-Driven Object Detection . | CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION, ICCV , 2023 , 2651-2661 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Facial Beauty Prediction (FBP) is subjective and varies from person to person, which makes it difficult to obtain a unified and objective evaluation. Previous efforts adopt conventional convolution neural networks to extract local facial features and calculate corresponding facial attractiveness scores, ignoring the global facial features. To address this issue, we propose a dynamic convolution vision transformer named FBPFormer which aims to focus on both local facial features and the global facial information of the human face. Specifically, we first build a lightweight convolution network to produce pseudo facial attribute embedding. To inject the global facial information into the transformer, the parameters of encoders are dynamically generated by the embedding of each instance. Therefore, these dynamic encoders can fuse and further fuse local facial features and global facial information while encoding query, key, and value vectors. Furthermore, we design an instance-level dynamic exponential loss to dynamically adjust the optimization objectives of the model. Extensive experiments show our method achieves competitive performance, demonstrating its effectiveness in the FBP task.
Keyword ：
Dynamic convolution Dynamic convolution Face beauty prediction Face beauty prediction Vision transformer Vision transformer
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Liu, Qipeng , Lin, Luojun , Shen, Zhifeng et al. FBPFormer: Dynamic Convolutional Transformer for Global-Local-Contexual Facial Beauty Prediction [J]. | ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING, ICANN 2023, PART X , 2023 , 14263 : 223-235 . |
| MLA | Liu, Qipeng et al. "FBPFormer: Dynamic Convolutional Transformer for Global-Local-Contexual Facial Beauty Prediction" . | ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING, ICANN 2023, PART X 14263 (2023) : 223-235 . |
| APA | Liu, Qipeng , Lin, Luojun , Shen, Zhifeng , Yu, Yuanlong . FBPFormer: Dynamic Convolutional Transformer for Global-Local-Contexual Facial Beauty Prediction . | ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING, ICANN 2023, PART X , 2023 , 14263 , 223-235 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Export
| Results： |
Selected to |
| Format： |
