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学者姓名：柯逍
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Action quality assessment (AQA) is a challenging vision task that requires discerning and quantifying subtle differences in actions from the same class. While recent research has made strides in creating fine-grained annotations for more precise analysis, existing methods primarily focus on coarse action segmentation, leading to limited identification of discriminative action frames. To address this issue, we propose a Vision-Language Action Knowledge Learning approach for action quality assessment, along with a multi-grained alignment framework to understand different levels of action knowledge. In our framework, prior knowledge, such as specialized terminology, is embedded into video-level, stage-level, and frame-level representations via CLIP. We further propose a new semantic-aware collaborative attention module to prevent confusing interactions and preserve textual knowledge in cross-modal and cross-semantic spaces. Specifically, we leverage the powerful cross-modal knowledge of CLIP to embed textual semantics into image features, which then guide action spatial-temporal representations. Our approach can be plug-and-played with existing AQA methods, frame-wise annotations or not. Extensive experiments and ablation studies show that our approach achieves state-of-the-art on four public short and long-term AQA benchmarks: FineDiving, MTL-AQA, JIGSAWS, and Fis-V.
Keyword ：
Action quality assessment Action quality assessment Semantic-aware learning Semantic-aware learning Vision-language pre-training Vision-language pre-training
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| GB/T 7714 | Xu, Huangbiao , Ke, Xiao , Li, Yuezhou et al. Vision-Language Action Knowledge Learning for Semantic-Aware Action Quality Assessment [J]. | COMPUTER VISION - ECCV 2024, PT XLII , 2025 , 15100 : 423-440 . |
| MLA | Xu, Huangbiao et al. "Vision-Language Action Knowledge Learning for Semantic-Aware Action Quality Assessment" . | COMPUTER VISION - ECCV 2024, PT XLII 15100 (2025) : 423-440 . |
| APA | Xu, Huangbiao , Ke, Xiao , Li, Yuezhou , Xu, Rui , Wu, Huanqi , Lin, Xiaofeng et al. Vision-Language Action Knowledge Learning for Semantic-Aware Action Quality Assessment . | COMPUTER VISION - ECCV 2024, PT XLII , 2025 , 15100 , 423-440 . |
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Accurate polyp segmentation is crucial for early diagnosis and treatment of colorectal cancer. This is a challenging task for three main reasons: (i) the problem of model overfitting and weak generalization due to the multi-center distribution of data; (ii) the problem of interclass ambiguity caused by motion blur and overexposure to endoscopic light; and (iii) the problem of intraclass inconsistency caused by the variety of morphologies and sizes of the same type of polyps. To address these challenges, we propose a new high-precision polyp segmentation framework, MEFA-Net, which consists of three modules, including the plug-and-play Mask Enhancement Module (MEG), Separable Path Attention Enhancement Module (SPAE), and Dynamic Global Attention Pool Module (DGAP). Specifically, firstly, the MEG module regionally masks the high-energy regions of the environment and polyps through a mask, which guides the model to rely on only a small amount of information to distinguish between polyps and background features, avoiding the model from overfitting the environmental information, and improving the robustness of the model. At the same time, this module can effectively counteract the 'dark corner phenomenon' in the dataset and further improve the generalization performance of the model. Next, the SPAE module can effectively alleviate the inter-class fuzzy problem by strengthening the feature expression. Then, the DGAP module solves the intra-class inconsistency problem by extracting the invariance of scale, shape and position. Finally, we propose a new evaluation metric, MultiColoScore, for comprehensively evaluating the segmentation performance of the model on five datasets with different domains. We evaluated the new method quantitatively and qualitatively on five datasets using four metrics. Experimental results show that MEFA-Net significantly improves the accuracy of polyp segmentation and outperforms current state-of-the-art algorithms. Code posted on https://github.com/847001315/MEFA-Net. © 2024
Keyword ：
Endoscopy Endoscopy Image coding Image coding Image segmentation Image segmentation Risk assessment Risk assessment
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| GB/T 7714 | Ke, Xiao , Chen, Guanhong , Liu, Hao et al. MEFA-Net: A mask enhanced feature aggregation network for polyp segmentation [J]. | Computers in Biology and Medicine , 2025 , 186 . |
| MLA | Ke, Xiao et al. "MEFA-Net: A mask enhanced feature aggregation network for polyp segmentation" . | Computers in Biology and Medicine 186 (2025) . |
| APA | Ke, Xiao , Chen, Guanhong , Liu, Hao , Guo, Wenzhong . MEFA-Net: A mask enhanced feature aggregation network for polyp segmentation . | Computers in Biology and Medicine , 2025 , 186 . |
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增强水下图像质量对水下作业领域的发展具有重要意义 . 现有的水下图像增强方法通常基于成对的水下图像和参考图像进行训练，然而实际获取与水下图像对应的参考图像比较困难，相比之下获得非成对高质量水下图像或者陆上图像较为容易. 此外，现有的水下图像增强方法很难同时针对各种失真类型进行图像增强. 为了避免对成对训练数据的依赖和进一步降低获得训练数据的难度，并应对多样的水下图像失真类型，本文提出了一种基于分频式生成对抗网络（Frequency-Decomposed Generative Adversarial Network，FD-GAN）的非成对水下图像增强方法，并在此基础上设计了高低频双分支生成器用于重建高质量水下增强图像. 具体来说，本文引入特征级别的小波变换将特征分为低频和高频部分，并基于循环一致性生成对抗网络对低频和高频部分区分处理. 其中，低频分支采用结合低频注意力机制的编码-解码器结构实现对图像颜色和亮度的增强，高频分支则采用并行的高频注意力机制对各高频分量进行增强，从而实现对图像细节的恢复. 在多个标准水下图像数据集上的实验结果表明，本文提出的方法在使用非成对的高质量水下图像和引入部分陆上图像的情况下，均能有效生成高质量的水下增强图像，且有效性和泛化性均优于当 前主流的水下图像增强方法.
Keyword ：
小波变换 小波变换 水下图像增强 水下图像增强 注意力机制 注意力机制 生成对抗网络 生成对抗网络 高低频双分支生成器 高低频双分支生成器
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| GB/T 7714 | 牛玉贞 , 张凌昕 , 兰杰 et al. 基于分频式生成对抗网络的非成对水下图像增强 [J]. | 电子学报 , 2025 . |
| MLA | 牛玉贞 et al. "基于分频式生成对抗网络的非成对水下图像增强" . | 电子学报 (2025) . |
| APA | 牛玉贞 , 张凌昕 , 兰杰 , 许瑞 , 柯逍 . 基于分频式生成对抗网络的非成对水下图像增强 . | 电子学报 , 2025 . |
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3D anomaly detection aims to solve the problem that image anomaly detection is greatly affected by lighting conditions. As commercial confidentiality and personal privacy become increasingly paramount, access to training samples is often restricted. To address these challenges, we propose a zero-shot 3D anomaly detection method. Unlike previous CLIP-based methods, the proposed method does not require any prompt and is capable of detecting anomalies on the depth modality. Furthermore, we also propose a pre-trained structural rerouting strategy, which modifies the transformer without retraining or fine-tuning for the anomaly detection task. Most importantly, this paper proposes an online voter mechanism that registers voters and performs majority voter scoring in a one-stage, zero-start and growth-oriented manner, enabling direct anomaly detection on unlabeled test sets. Finally, we also propose a confirmatory judge credibility assessment mechanism, which provides an efficient adaptation for possible few-shot conditions. Results on datasets such as MVTec3D-AD demonstrate that the proposed method can achieve superior zero-shot 3D anomaly detection performance, indicating its pioneering contributions within the pertinent domain.
Keyword ：
Anomaly detection Anomaly detection Multimodal Multimodal Online voter mechanism Online voter mechanism Pretrained model Pretrained model Zero-shot Zero-shot
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| GB/T 7714 | Zheng, Wukun , Ke, Xiao , Guo, Wenzhong . Zero-shot 3D anomaly detection via online voter mechanism [J]. | NEURAL NETWORKS , 2025 , 187 . |
| MLA | Zheng, Wukun et al. "Zero-shot 3D anomaly detection via online voter mechanism" . | NEURAL NETWORKS 187 (2025) . |
| APA | Zheng, Wukun , Ke, Xiao , Guo, Wenzhong . Zero-shot 3D anomaly detection via online voter mechanism . | NEURAL NETWORKS , 2025 , 187 . |
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Fine-grained visual classification (FGVC) is a highly challenging task that aims to learn subtle differences between visually similar objects. Most existing methods for FGVC rely on deep convolutional neural networks to mine local fine-grained features, which neglect the learning of relationships between global and local semantics. Moreover, the feature encoding stage inevitably constructs complex feature representations, leading to overfitting to specific feature patterns, which is not beneficial for fine-grained visual classification. To address these issues, we propose a Transformer-based FGVC model, called the Multi-Granularity Interaction and Feature Recombination Network(MGIFR-Net), which consists of three modules. Firstly, a self-attention guided localization module is designed to locate and amplify discriminative local regions, enabling the sufficient learning of local detail information. Secondly, to enhance the perception of multi-granularity semantic interaction information, we construct a multi-granularity feature interaction learning module to jointly learn local and global feature representations. Finally, a dynamic feature recombination enhancement method is proposed, which explores diverse feature pattern combinations while retaining invariant features, effectively alleviating the overfitting problem caused by complex feature representations. Our method achieves stateof-the-art performance on four benchmark FGVC datasets (CUB-200-2011, Stanford Cars, FGVC-Aircraft, and NAbirds), and experimental results demonstrate the superiority of our method on different visual classification benchmarks.
Keyword ：
Feature recombination Feature recombination Fine-grained visual classification Fine-grained visual classification Multi-granularity feature interaction Multi-granularity feature interaction Vision transformer Vision transformer
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| GB/T 7714 | Ke, Xiao , Cai, Yuhang , Chen, Baitao et al. Multi-granularity interaction and feature recombination network for fine-grained visual classification [J]. | PATTERN RECOGNITION , 2025 , 166 . |
| MLA | Ke, Xiao et al. "Multi-granularity interaction and feature recombination network for fine-grained visual classification" . | PATTERN RECOGNITION 166 (2025) . |
| APA | Ke, Xiao , Cai, Yuhang , Chen, Baitao , Liu, Hao , Guo, Wenzhong . Multi-granularity interaction and feature recombination network for fine-grained visual classification . | PATTERN RECOGNITION , 2025 , 166 . |
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Enhancing the quality of underwater images is crucial for advancements in the fields of underwater exploration and underwater rescue. Existing underwater image enhancement methods typically rely on paired underwater images and reference images for training. However, obtaining corresponding reference images for underwater images is challenging in practice. In contrast, acquiring high-quality unpaired underwater images or images captured on land are relatively more straightforward. Furthermore, existing techniques for underwater image enhancement often struggle to address a variety of distortion types simultaneously. To avoid the reliance on paired training data, reduce the difficulty of acquiring training data, and effectively handle diverse types of underwater image distortions, in this paper, we propose a novel unpaired underwater image enhancement method based on the frequency-decomposed generative adversarial network (FD-GAN). We design a dual-branch generator based on high and low frequencies to reconstruct high-quality underwater images. Specifically, feature-level wavelet transform is introduced to separate the features into low-frequency and high-frequency parts. Then the separated features are processed by a cycle-consistent generative adversarial network, so as to simultaneously enhance the color and luminance in the low-frequency component and details in the high-frequency part. More specific, the low-frequency branch employs an encoder-decoder structure with a low-frequency attention mechanism to enhance the color and brightness of the image. The high-frequency branch utilizes parallel high-frequency attention mechanisms to enhance various high-frequency components, thereby achieving the restoration of image details. Experimental results on multiple datasets show that the proposed method trained with unpaired high-quality underwater images or unpaired high-quality underwater images and on-land images, can effectively generate high-quality underwater enhanced images and the proposed method is superior to the state-of-the-art underwater image enhancement methods in terms of effectiveness and generalization. © 2025 Chinese Institute of Electronics. All rights reserved.
Keyword ：
Color image processing Color image processing Image coding Image coding Image compression Image compression Image enhancement Image enhancement Photointerpretation Photointerpretation Underwater photography Underwater photography Wavelet decomposition Wavelet decomposition
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| GB/T 7714 | Niu, Yu-Zhen , Zhang, Ling-Xin , Lan, Jie et al. FD-GAN: Frequency-Decomposed Generative Adversarial Network for Unpaired Underwater Image Enhancement [J]. | Acta Electronica Sinica , 2025 , 53 (2) : 527-544 . |
| MLA | Niu, Yu-Zhen et al. "FD-GAN: Frequency-Decomposed Generative Adversarial Network for Unpaired Underwater Image Enhancement" . | Acta Electronica Sinica 53 . 2 (2025) : 527-544 . |
| APA | Niu, Yu-Zhen , Zhang, Ling-Xin , Lan, Jie , Xu, Rui , Ke, Xiao . FD-GAN: Frequency-Decomposed Generative Adversarial Network for Unpaired Underwater Image Enhancement . | Acta Electronica Sinica , 2025 , 53 (2) , 527-544 . |
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The fair and objective assessment of performances and competitions is a common pursuit and challenge in human society. The application of computer vision technology offers hope for this purpose, but it still faces obstacles such as occlusion and motion blur. To address these hindrances, our DanceFix proposes a bidirectional spatial-temporal context optical flow correction (BOFC) method. This approach leverages the consistency and complementarity of motion information between two modalities: optical flow, which excels at pixel capture, and lightweight skeleton data. It enables the extraction of pixel-level motion changes and the correction of abnormal skeleton data. Furthermore, we propose a part-level dance dataset (Dancer Parts) and part-level motion feature extraction based on task decoupling (PETD). This aims to decouple complex whole-body parts tracking into fine-grained limb-level motion extraction, enhancing the confidence of temporal information and the accuracy of correction for abnormal data. Finally, we present the DNV dataset, which simulates fully neat group dance scenes and provides reliable labels and validation methods for the newly introduced group dance neatness assessment (GDNA). To the best of our knowledge, this is the first work to develop quantitative criteria for assessing limb and joint neatness in group dance. We conduct experiments on DNV and video-based public JHMDB datasets. Our method effectively corrects abnormal skeleton points, flexibly embeds, and improves the accuracy of existing pose estimation algorithms. Copyright © 2025, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
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| GB/T 7714 | Xu, Huangbiao , Ke, Xiao , Wu, Huanqi et al. DanceFix: An Exploration in Group Dance Neatness Assessment Through Fixing Abnormal Challenges of Human Pose [C] . 2025 : 8869-8877 . |
| MLA | Xu, Huangbiao et al. "DanceFix: An Exploration in Group Dance Neatness Assessment Through Fixing Abnormal Challenges of Human Pose" . (2025) : 8869-8877 . |
| APA | Xu, Huangbiao , Ke, Xiao , Wu, Huanqi , Xu, Rui , Li, Yuezhou , Xu, Peirong et al. DanceFix: An Exploration in Group Dance Neatness Assessment Through Fixing Abnormal Challenges of Human Pose . (2025) : 8869-8877 . |
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Action quality assessment (AQA) is a challenging vision task due to the complexity and variance of the scoring rules embedded in the videos. Recent approaches have reduced the prediction difficulty of AQA via learning action differences between videos, but there are still challenges in learning scoring rules and capturing feature differences. To address these challenges, we propose a two -path target -aware contrastive regression (T2CR) framework. We propose to fuse direct and contrastive regression and exploit the consistency of information across multiple visual fields. Specifically, we first directly learn the relational mapping between global video features and scoring rules, which builds occupational domain prior knowledge to better capture local differences between videos. Then, we acquire the auxiliary visual fields of the videos through sparse sampling to learn the commonality of feature representations in multiple visual fields and eliminate the effect of subjective noise from a single visual field. To demonstrate the effectiveness of T2CR, we conduct extensive experiments on four AQA datasets (MTL-AQA, FineDiving, AQA-7, JIGSAWS). Our method is superior to state-of-the-art methods without elaborate structural design and fine-grained information.
Keyword ：
Action quality assessment Action quality assessment Multi-view information Multi-view information Video understanding Video understanding
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| GB/T 7714 | Ke, Xiao , Xu, Huangbiao , Lin, Xiaofeng et al. Two-path target-aware contrastive regression for action quality assessment [J]. | INFORMATION SCIENCES , 2024 , 664 . |
| MLA | Ke, Xiao et al. "Two-path target-aware contrastive regression for action quality assessment" . | INFORMATION SCIENCES 664 (2024) . |
| APA | Ke, Xiao , Xu, Huangbiao , Lin, Xiaofeng , Guo, Wenzhong . Two-path target-aware contrastive regression for action quality assessment . | INFORMATION SCIENCES , 2024 , 664 . |
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Text -based person search aims to use text descriptions to search for corresponding person images. However, due to the obvious pattern differences in image and text modalities, it is still a challenging problem to align the two modalities. Most existing approaches only consider semantic alignment within a global context or partial parts, lacking consideration of how to match image and text in terms of differences in model information. Therefore, in this paper, we propose an efficient Modality -Aligned Person Search network (MAPS) to address this problem. First, we suppress image -specific information by image feature style normalization to achieve modality knowledge alignment and reduce information differences between text and image. Second, we design a multi -granularity modal feature fusion and optimization method to enrich the modal features. To address the problem of useless and redundant information in the multi -granularity fused features, we propose a Multigranularity Feature Self -optimization Module (MFSM) to adaptively adjust the corresponding contributions of different granularities in the fused features of the two modalities. Finally, to address the problem of information inconsistency in the training and inference stages, we propose a Cross -instance Feature Alignment (CFA) to help the network enhance category -level generalization ability and improve retrieval performance. Extensive experiments demonstrate that our MAPS achieves state-of-the-art performance on all text -based person search datasets, and significantly outperforms other existing methods.
Keyword ：
CNN CNN Cross-modality Cross-modality Image-text retrieval Image-text retrieval Person re-identification Person re-identification
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| GB/T 7714 | Ke, Xiao , Liu, Hao , Xu, Peirong et al. Text-based person search via cross-modal alignment learning [J]. | PATTERN RECOGNITION , 2024 , 152 . |
| MLA | Ke, Xiao et al. "Text-based person search via cross-modal alignment learning" . | PATTERN RECOGNITION 152 (2024) . |
| APA | Ke, Xiao , Liu, Hao , Xu, Peirong , Lin, Xinru , Guo, Wenzhong . Text-based person search via cross-modal alignment learning . | PATTERN RECOGNITION , 2024 , 152 . |
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Image hiding aims to conceal one or more secret images within a cover image of the same resolution. Due to strict capacity requirements, image hiding is commonly called large-capacity steganography. In this paper, we propose StegFormer, a novel autoencoder-based image-hiding model. StegFormer can conceal one or multiple secret images within a cover image of the same resolution while preserving the high visual quality of the stego image. In addition, to mitigate the limitations of current steganographic models in real-world scenarios, we propose a normalizing training strategy and a restrict loss to improve the reliability of the steganographic models under realistic conditions. Furthermore, we propose an efficient steganographic capacity expansion method to increase the capacity of steganography and enhance the efficiency of secret communication. Through this approach, we can increase the relative payload of StegFormer to 96 bits per pixel without any training strategy modifications. Experiments demonstrate that our StegFormer outperforms existing state-of-the-art (SOTA) models. In the case of single-image steganography, there is an improvement of more than 3 dB and 5 dB in PSNR for secret/recovery image pairs and cover/stego image pairs. Copyright © 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Keyword ：
Artificial intelligence Artificial intelligence Image enhancement Image enhancement Learning systems Learning systems Steganography Steganography
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| GB/T 7714 | Ke, Xiao , Wu, Huanqi , Guo, Wenzhong . StegFormer: Rebuilding the Glory of Autoencoder-Based Steganography [C] . 2024 : 2723-2731 . |
| MLA | Ke, Xiao et al. "StegFormer: Rebuilding the Glory of Autoencoder-Based Steganography" . (2024) : 2723-2731 . |
| APA | Ke, Xiao , Wu, Huanqi , Guo, Wenzhong . StegFormer: Rebuilding the Glory of Autoencoder-Based Steganography . (2024) : 2723-2731 . |
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