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Yaxiong Chen

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9 papers

2 author rows

EAAI Journal 2026 Journal Article

Exploring a double task learning framework for makeup transfer

Zhaoyang Sun
Shengwu Xiong
Yaxiong Chen
Yi Rong

Although impressive progress has been made with current makeup transfer methods, they are still facing two main challenges that have not been effectively addressed: (1) Due to the lack of real transferred targets, existing methods attempt to synthesize Pseudo Ground Truths (PGTs) to supervise the model training. Therefore, their performance is heavily dependent on the synthesis quality of PGTs. (2) Most previous works fail to achieve semantic alignment between the high-resolution feature maps of the source and reference images. As a result, some high-frequency makeup details will be lost, limiting their ability to deal with diverse makeup styles. In this paper, we propose a Double Task Makeup Transfer (DTMT) framework to handle these two challenges. Specifically, for the first one, DTMT jointly optimizes an unsupervised main makeup transfer task along with a self-supervised auxiliary reconstruction task to avoid the negative effects of sub-optimal PGTs. For the second challenge, we develop a novel Divide and Conquer Attention (DC-Attention) operation, which semantically aligns the high-resolution feature maps of the input images through a coarse-to-fine procedure. Compared to the traditional cross-attention operation, our DC-Attention has much less computational overhead and thus can be more efficient to process high-resolution feature maps. Extensive experiments on three publicly available datasets indicate that DTMT significantly outperforms eight benchmark methods in both quantitative and qualitative evaluations, promoting the development of artificial intelligence virtual makeup try-on. Moreover, DTMT demonstrates strong generalization and control capabilities for makeup styles, advancing the engineering applications of makeup transfer. Our code is available at https: //github. com/Snowfallingplum/DTMT.

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AAAI Conference 2026 Conference Paper

ProPL: Universal Semi-Supervised Ultrasound Image Segmentation via Prompt-Guided Pseudo-Labeling

Yaxiong Chen
Qicong Wang
Chunlei Li
Jingliang Hu
Yilei Shi
Shengwu Xiong
Xiao Xiang Zhu
Lichao Mou

Existing approaches for the problem of ultrasound image segmentation, whether supervised or semi-supervised, are typically specialized for specific anatomical structures or tasks, limiting their practical utility in clinical settings. In this paper, we pioneer the task of universal semi-supervised ultrasound image segmentation and propose ProPL, a framework that can handle multiple organs and segmentation tasks while leveraging both labeled and unlabeled data. At its core, ProPL employs a shared vision encoder coupled with prompt-guided dual decoders, enabling flexible task adaptation through a prompting-upon-decoding mechanism and reliable self-training via an uncertainty-driven pseudo-label calibration (UPLC) module. To facilitate research in this direction, we introduce a comprehensive ultrasound dataset spanning 5 organs and 8 segmentation tasks. Extensive experiments demonstrate that ProPL outperforms state-of-the-art methods across various metrics, establishing a new benchmark for universal ultrasound image segmentation.

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ECAI Conference 2025 Conference Paper

LAMM-ViT: AI Face Detection via Layer-Aware Modulation of Region-Guided Attention

Jiangling Zhang
Weijie Zhu
Jirui Huang
Yaxiong Chen

Detecting AI-synthetic faces presents a critical challenge: it is hard to capture consistent structural relationships between facial regions across diverse generation techniques. Current methods, which focus on specific artifacts rather than fundamental inconsistencies, often fail when confronted with novel generative models. To address this limitation, we introduce Layer-aware Mask Modulation Vision Transformer (LAMM-ViT), a Vision Transformer designed for robust facial forgery detection. This model integrates distinct Region-Guided Multi-Head Attention (RG-MHA) and Layer-aware Mask Modulation (LAMM) components within each layer. RG-MHA utilizes facial landmarks to create regional attention masks, guiding the model to scrutinize architectural inconsistencies across different facial areas. Crucially, the separate LAMM module dynamically generates layer-specific parameters, including mask weights and gating values, based on network context. These parameters then modulate the behavior of RG-MHA, enabling adaptive adjustment of regional focus across network depths. This architecture facilitates the capture of subtle, hierarchical forgery cues ubiquitous among diverse generation techniques, such as GANs and Diffusion Models. In cross-model generalization tests, LAMM-ViT demonstrates superior performance, achieving 94. 09% mean ACC (a +5. 45% improvement over SoTA) and 98. 62% mean AP (a +3. 09% improvement). These results demonstrate LAMM-ViT’s exceptional ability to generalize and its potential for reliable deployment against evolving synthetic media threats. The code is available at https: //github. com/WHUT-ZJL/LAMM-ViT.

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AAAI Conference 2025 Conference Paper

RealisID: Scale-Robust and Fine-Controllable Identity Customization via Local and Global Complementation

Zhaoyang Sun
Fei Du
Weihua Chen
Fan Wang
Yaxiong Chen
Yi Rong
Shengwu Xiong

Recently, the success of text-to-image synthesis has greatly advanced the development of identity customization techniques, whose main goal is to produce realistic identity-specific photographs based on text prompts and reference face images. However, it is difficult for existing identity customization methods to simultaneously meet the various requirements of different real-world applications, including the identity fidelity of small face, the control of face location, pose and expression, as well as the customization of multiple persons. To this end, we propose a scale-robust and fine-controllable method, namely RealisID, which learns different control capabilities through the cooperation between a pair of local and global branches. Specifically, by using cropping and up-sampling operations to filter out face-irrelevant information, the local branch concentrates the fine control of facial details and the scale-robust identity fidelity within the face region. Meanwhile, the global branch manages the overall harmony of the entire image. It also controls the face location by taking the location guidance as input. As a result, RealisID can benefit from the complementarity of these two branches. Finally, by implementing our branches with two different variants of ControlNet, our method can be easily extended to handle multi-person customization, even only trained on single-person datasets. Extensive experiments and ablation studies indicate the effectiveness of RealisID and verify its ability in fulfilling all the requirements mentioned above.

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EAAI Journal 2024 Journal Article

A Fine Rendering High-Resolution Makeup Transfer network via inversion-editing strategy

Zhaoyang Sun
Shengwu Xiong
Yaxiong Chen
Yi Rong

While current makeup transfer methods have made progress in realism and color fidelity, they struggle with capturing texture details and producing high-resolution images, limiting their practical utility. To address these challenges, we propose a Fine Rendering High-resolution Makeup Transfer (FRHMT) network, which leverages a powerful style-based generator and introduces a novel inversion-editing strategy tailored for makeup transfer. Concretely, in the inversion phase, considering the semantic decoupling properties in the latent space, we design a Hierarchical Residual Inversion (HRI), which projects the image onto high-dimensional feature maps in coarse layers and low-dimensional style codes in fine layers. This design effectively restores the content information of the image while maintaining flexibility in editing the makeup styles. In the editing phase, the Makeup Modulation Module (MMM) learns two mapping networks to adjust the latent variables of the source image based on those of the reference image. This modification occurs in fine layers to transfer the makeup information and preserve the content information. With new network structures and customized loss functions, our training eliminates cumbersome pseudo-paired data synthesis and unstable adversarial learning. Extensive experiments have demonstrated that our method outperforms existing methods in both image quality and makeup similarity through quantitative and qualitative analysis. Additionally, we address the lack of high-resolution data by collecting a dataset of 9716 face images with a resolution of 1024 × 1024. In conclusion, our framework offers a novel artificial intelligence (AI) implementation of makeup transfer in engineering, with the collected dataset holding substantial value for further advancements in AI research.

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NeurIPS Conference 2024 Conference Paper

SHMT: Self-supervised Hierarchical Makeup Transfer via Latent Diffusion Models

Zhaoyang Sun
Shengwu Xiong
Yaxiong Chen
Fei Du
Weihua Chen
Fan Wang
Yi Rong

This paper studies the challenging task of makeup transfer, which aims to apply diverse makeup styles precisely and naturally to a given facial image. Due to the absence of paired data, current methods typically synthesize sub-optimal pseudo ground truths to guide the model training, resulting in low makeup fidelity. Additionally, different makeup styles generally have varying effects on the person face, but existing methods struggle to deal with this diversity. To address these issues, we propose a novel Self-supervised Hierarchical Makeup Transfer (SHMT) method via latent diffusion models. Following a "decoupling-and-reconstruction" paradigm, SHMT works in a self-supervised manner, freeing itself from the misguidance of imprecise pseudo-paired data. Furthermore, to accommodate a variety of makeup styles, hierarchical texture details are decomposed via a Laplacian pyramid and selectively introduced to the content representation. Finally, we design a novel Iterative Dual Alignment (IDA) module that dynamically adjusts the injection condition of the diffusion model, allowing the alignment errors caused by the domain gap between content and makeup representations to be corrected. Extensive quantitative and qualitative analyses demonstrate the effectiveness of our method. Our code is available at https: //github. com/Snowfallingplum/SHMT.

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AAAI Conference 2023 Conference Paper

ESPT: A Self-Supervised Episodic Spatial Pretext Task for Improving Few-Shot Learning

Yi Rong
Xiongbo Lu
Zhaoyang Sun
Yaxiong Chen
Shengwu Xiong

Self-supervised learning (SSL) techniques have recently been integrated into the few-shot learning (FSL) framework and have shown promising results in improving the few-shot image classification performance. However, existing SSL approaches used in FSL typically seek the supervision signals from the global embedding of every single image. Therefore, during the episodic training of FSL, these methods cannot capture and fully utilize the local visual information in image samples and the data structure information of the whole episode, which are beneficial to FSL. To this end, we propose to augment the few-shot learning objective with a novel self-supervised Episodic Spatial Pretext Task (ESPT). Specifically, for each few-shot episode, we generate its corresponding transformed episode by applying a random geometric transformation to all the images in it. Based on these, our ESPT objective is defined as maximizing the local spatial relationship consistency between the original episode and the transformed one. With this definition, the ESPT-augmented FSL objective promotes learning more transferable feature representations that capture the local spatial features of different images and their inter-relational structural information in each input episode, thus enabling the model to generalize better to new categories with only a few samples. Extensive experiments indicate that our ESPT method achieves new state-of-the-art performance for few-shot image classification on three mainstay benchmark datasets. The source code will be available at: https://github.com/Whut-YiRong/ESPT.

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AAAI Conference 2022 Conference Paper

SSAT: A Symmetric Semantic-Aware Transformer Network for Makeup Transfer and Removal

Zhaoyang Sun
Yaxiong Chen
Shengwu Xiong

Makeup transfer is not only to extract the makeup style of the reference image, but also to render the makeup style to the semantic corresponding position of the target image. However, most existing methods focus on the former and ignore the latter, resulting in a failure to achieve desired results. To solve the above problems, we propose a unified Symmetric Semantic-Aware Transformer (SSAT) network, which incorporates semantic correspondence learning to realize makeup transfer and removal simultaneously. In SSAT, a novel Symmetric Semantic Corresponding Feature Transfer (SSCFT) module and a weakly supervised semantic loss are proposed to model and facilitate the establishment of accurate semantic correspondence. In the generation process, the extracted makeup features are spatially distorted by SSCFT to achieve semantic alignment with the target image, then the distorted makeup features are combined with unmodified makeup irrelevant features to produce the final result. Experiments show that our method obtains more visually accurate makeup transfer results, and user study in comparison with other state-ofthe-art makeup transfer methods reflects the superiority of our method. Besides, we verify the robustness of the proposed method in the difference of expression and pose, object occlusion scenes, and extend it to video makeup transfer.

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EAAI Journal 2021 Journal Article

A bi-level distribution mixture framework for unsupervised driving performance evaluation from naturalistic truck driving data

Lin Lu
Shengwu Xiong
Yaxiong Chen

Driving performance evaluations can contribute to fleet management and lead to safer and more economical driving conditions for manned or driverless fleet vehicles. One approach to driving performance evaluation involves quantitative mapping or categorical labeling of skill levels and categorizing of driving patterns from extraordinarily mild to the most aggressive. This paper presents a big data system for driving performance evaluations of drivers and trips using a probabilistic framework. The proposed framework combines a feature mixture model for scoring driving performance through defined objective comparison criteria and a latent style mixture model for classifying drivers by the main driving styles they exhibit. To demonstrate the effectiveness of the proposed models, we perform both quantitative and qualitative experiments. The results show that the former produces an interpretable and normal scorecard model, while the latter helps build an improved clustering model that represents enhanced driver behavior.

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