Pattern Recognition最新文献_第6页

Deep clustering via dual-supervised multi-kernel mapping

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-02-04 DOI: 10.1016/j.patcog.2025.111419

Lina Ren , Ruizhang Huang , Shengwei Ma , Yongbin Qin , Yanping Chen , Chuan Lin

{"title":"Deep clustering via dual-supervised multi-kernel mapping","authors":"Lina Ren , Ruizhang Huang , Shengwei Ma , Yongbin Qin , Yanping Chen , Chuan Lin","doi":"10.1016/j.patcog.2025.111419","DOIUrl":"10.1016/j.patcog.2025.111419","url":null,"abstract":"<div><div>In this paper, we propose a novel deep clustering framework via dual-supervised multi-kernel mapping, namely DCDMK, to improve clustering performance by learning linearly structural separable data representations. In the DCDMK framework, we introduce a kernel-aid encoder comprising two key components: a semantic representation learner, which captures the essential semantic information for clustering, and a multi-kernel representation learner, which dynamically selects the optimal combination of kernel functions through dual-supervised multi-kernel mapping to learn structurally separable kernel representations. The dual self-supervised mechanism is devised to jointly optimize both kernel representation learning and structural partitioning. Based on this framework, we introduce different fusion strategies to learn the multi-kernel representation of data samples for the clustering task. We derive two variants, namely DCDMK-WL (with layer-level kernel representation learning) and DCDMK-OL (without layer-level kernel representation learning). Extensive experiments on six real-world datasets demonstrate the effectiveness of our DCDMK framework.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"162 ","pages":"Article 111419"},"PeriodicalIF":7.5,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143298593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interpretable multi-view clustering

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-02-03 DOI: 10.1016/j.patcog.2025.111418

Mudi Jiang , Lianyu Hu , Zengyou He , Zhikui Chen

{"title":"Interpretable multi-view clustering","authors":"Mudi Jiang , Lianyu Hu , Zengyou He , Zhikui Chen","doi":"10.1016/j.patcog.2025.111418","DOIUrl":"10.1016/j.patcog.2025.111418","url":null,"abstract":"<div><div>Multi-view clustering has become a significant area of research, with numerous methods proposed over the past decades to enhance clustering accuracy. However, in many real-world applications, it is crucial to demonstrate a clear decision-making process-specifically, explaining why samples are assigned to particular clusters. Consequently, there remains a notable gap in developing interpretable methods for clustering multi-view data. To fill this crucial gap, we make the first attempt towards this direction by introducing an interpretable multi-view clustering framework. Our method begins by extracting embedded features from each view and generates pseudo-labels to guide the initial construction of the decision tree. Subsequently, it iteratively optimizes the feature representation for each view along with refining the interpretable decision tree. Experimental results on real datasets demonstrate that our method not only provides a transparent clustering process for multi-view data but also delivers performance comparable to state-of-the-art multi-view clustering methods. To the best of our knowledge, this is the first effort to design an interpretable clustering framework specifically for multi-view data, opening a new avenue in this field.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"162 ","pages":"Article 111418"},"PeriodicalIF":7.5,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143298461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generative feature style augmentation for domain generalization in medical image segmentation

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-02-03 DOI: 10.1016/j.patcog.2025.111416

Yunzhi Huang , Luyi Han , Haoran Dou

{"title":"Generative feature style augmentation for domain generalization in medical image segmentation","authors":"Yunzhi Huang , Luyi Han , Haoran Dou","doi":"10.1016/j.patcog.2025.111416","DOIUrl":"10.1016/j.patcog.2025.111416","url":null,"abstract":"<div><div>Although learning-based models have achieved tremendous success in medical image segmentation for independent and identical distributed data, model performance often deteriorates for out-of-distribution data. Training a model for each domain requires extra time and computing resources and increases the annotation burden of physicians. It is hence more practical to generalize the segmentation model trained using a single source domain. In this work, we model domain-level feature style with a flexible probabilistic block, that is framework-agnostic and can be integrated into an arbitrary segmentation network to enhance the model generality on unseen datasets. Specifically, we employ a variational auto-encoder to learn the feature style representations, enabling the generation of diverse feature styles through sampling from a prior distribution. During inference, we replace the target feature style with that of the source domain using a linear transformation. We compare our method with five state-of-the-art domain generalization (DG) methods using prostate MRI data from six centers and spinal cord MRI data from four sites. Evaluation with Dice similarity coefficient score and 95th percentile Hausdorff distance demonstrates that our method achieves superior improvement in model generalizability over other DG models.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"162 ","pages":"Article 111416"},"PeriodicalIF":7.5,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143298460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Beyond boundaries: Hierarchical-contrast unsupervised temporal action localization with high-coupling feature learning

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-02-03 DOI: 10.1016/j.patcog.2025.111421

Yuanyuan Liu , Ning Zhou , Yuxuan Huang , Shuyang Liu , Leyuan Liu , Wujie Zhou , Chang Tang , Ke Wang

{"title":"Beyond boundaries: Hierarchical-contrast unsupervised temporal action localization with high-coupling feature learning","authors":"Yuanyuan Liu , Ning Zhou , Yuxuan Huang , Shuyang Liu , Leyuan Liu , Wujie Zhou , Chang Tang , Ke Wang","doi":"10.1016/j.patcog.2025.111421","DOIUrl":"10.1016/j.patcog.2025.111421","url":null,"abstract":"<div><div>Current unsupervised temporal action localization (UTAL) methods mainly use clustering and localization with independent learning mechanisms. However, these individual mechanisms are low-coupled and struggle to finely localize action-background boundary information due to the lack of feature interactions in the clustering and localization process. To address this, we propose an end-to-end Hierarchical-Contrast UTAL (HC-UTAL) framework with high-coupling multi-task feature learning. HC-UTAL incorporates coarse-to-fine contrastive learning (CL) at three levels: <em>video level</em>, <em>instance level</em> and <em>boundary level</em>, thus obtaining adaptive interaction and robust performance. We first employ the <em>video-level CL</em> on video-level and cluster-level feature learning, generating video action pseudo-labels. Then, using the video action pseudo-labels, we further devise the <em>instance-level CL</em> on action-related feature learning for coarse localization and the <em>boundary-level CL</em> on ambiguous action-background boundary feature learning for finer localization, respectively. We conduct extensive experiments on THUMOS’14, ActivityNet v1.2, and ActivityNet v1.3 datasets. The results demonstrate that our method achieves state-of-the-art performance. The code and trained models are available at: <span><span>https://github.com/bugcat9/HC-UTAL</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"162 ","pages":"Article 111421"},"PeriodicalIF":7.5,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Variable multi-scale attention fusion network and adaptive correcting gradient optimization for multi-task learning

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-02-01 DOI: 10.1016/j.patcog.2025.111423

Naihua Ji , Yongqiang Sun , Fanyun Meng , Liping Pang , Yuzhu Tian

{"title":"Variable multi-scale attention fusion network and adaptive correcting gradient optimization for multi-task learning","authors":"Naihua Ji , Yongqiang Sun , Fanyun Meng , Liping Pang , Yuzhu Tian","doi":"10.1016/j.patcog.2025.111423","DOIUrl":"10.1016/j.patcog.2025.111423","url":null,"abstract":"<div><div>Network architecture and optimization are two indispensable parts in multi-task learning, which together improve the performance of multi-task learning. Previous work has rarely focused on both aspects simultaneously. In this paper, we analyze the multi-task learning from network architecture and optimization. In network architecture aspect, we propose a variable multi-scale attention fusion network, which overcomes the issue of feature loss when processing small-scale feature maps during upsampling and resolves the problem of inadequate learning in conventional multi-scale models due to significant spatial size disparities. In optimization aspect, a adaptive correcting gradient scheme is put forward to treat the defects of conflicts and dominance among multiple tasks during the process of training, and it effectively alleviates the imbalance of multi-task training. Various ablation experiments and comparative experiments demonstrate that simultaneously considering the network framework and optimization can make great improvement for the performance of multi-task learning. Our code is available at <span><span>https://github.com/SyqxhSt/Net-Opt-MTL</span><svg><path></path></svg></span></div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"162 ","pages":"Article 111423"},"PeriodicalIF":7.5,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143150570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A closer look at the explainability of Contrastive language-image pre-training

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-02-01 DOI: 10.1016/j.patcog.2025.111409

Yi Li , Hualiang Wang , Yiqun Duan , Jiheng Zhang , Xiaomeng Li

{"title":"A closer look at the explainability of Contrastive language-image pre-training","authors":"Yi Li , Hualiang Wang , Yiqun Duan , Jiheng Zhang , Xiaomeng Li","doi":"10.1016/j.patcog.2025.111409","DOIUrl":"10.1016/j.patcog.2025.111409","url":null,"abstract":"<div><div>Contrastive language-image pre-training (CLIP) is a powerful vision-language model that has shown great benefits for various tasks. However, we have identified some issues with its explainability, which undermine its credibility and limit the capacity for related tasks. Specifically, we find that CLIP tends to focus on background regions rather than foregrounds, with noisy activations at irrelevant positions on the visualization results. These phenomena conflict with conventional explainability methods based on the class attention map (CAM), where the raw model can highlight the local foreground regions using global supervision without alignment. To address these problems, we take a closer look at its architecture and features. Our analysis revealed that raw self-attentions link to inconsistent semantic regions, resulting in the opposite visualization. Besides, the noisy activations stem from redundant features among categories. Building on these insights, we propose the CLIP Surgery for reliable CAM, a method that allows surgery-like modifications to the inference architecture and features, without further fine-tuning as classical CAM methods. This approach significantly improves the explainability of CLIP, surpassing existing methods by large margins. Besides, it enables multimodal visualization and extends the capacity of raw CLIP on open-vocabulary tasks without extra alignment. The code is available at <span><span>https://github.com/xmed-lab/CLIP_Surgery</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"162 ","pages":"Article 111409"},"PeriodicalIF":7.5,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143149859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HiPPO: Enhancing proximal policy optimization with highlight replay

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-01-31 DOI: 10.1016/j.patcog.2025.111408

Shutong Zhang , Xing Chen , Zhaogeng Liu , Hechang Chen , Yi Chang

{"title":"HiPPO: Enhancing proximal policy optimization with highlight replay","authors":"Shutong Zhang , Xing Chen , Zhaogeng Liu , Hechang Chen , Yi Chang","doi":"10.1016/j.patcog.2025.111408","DOIUrl":"10.1016/j.patcog.2025.111408","url":null,"abstract":"<div><div>Sample efficiency remains a paramount challenge in policy gradient methods within reinforcement learning. The success of experience replay demonstrates the importance of leveraging historical experiences, often through off-policy methods to enhance approximate policy learning algorithms that aim to maximize current interaction sample reuse, aligning approximate policies with target objectives. However, the inaccurate approximation can negatively affect actual optimization, leading to poorer current experiences than past ones. We propose Highlight Replay Enhanced Proximal Policy Optimization (HiPPO) to address the challenge. Specifically, HiPPO optimizes by highlighting policies and introducing a penalty reward function for constrained optimization, which alleviates the constraints of policy similarity and boosts adaptability to historical experiences. Empirical studies show HiPPO outperforming state-of-the-art algorithms in MuJoCo continuous tasks in performance and learning speed. An in-depth analysis of the experimental results validates the effectiveness of employing highlight replay and penalty reward functions in our proposed method.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"162 ","pages":"Article 111408"},"PeriodicalIF":7.5,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143149493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TFDNet: Time–Frequency enhanced Decomposed Network for long-term time series forecasting

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-01-31 DOI: 10.1016/j.patcog.2025.111412

Yuxiao Luo , Songming Zhang , Ziyu Lyu , Yuhan Hu

{"title":"TFDNet: Time–Frequency enhanced Decomposed Network for long-term time series forecasting","authors":"Yuxiao Luo , Songming Zhang , Ziyu Lyu , Yuhan Hu","doi":"10.1016/j.patcog.2025.111412","DOIUrl":"10.1016/j.patcog.2025.111412","url":null,"abstract":"<div><div>Long-term time series forecasting is a vital task and applicable across diverse fields. Recent methods focus on capturing the underlying patterns from one single domain (e.g. the time domain or the frequency domain) without a holistic view to process long-term time series from the time–frequency domains. In this paper, we propose a <strong>T</strong>ime-<strong>F</strong>requency enhanced <strong>D</strong>ecomposed <strong>Net</strong>work (<strong>TFDNet</strong>) to capture both the long-term temporal variations and periodicity from the time–frequency domain. In TFDNet, we devise a multi-scale time–frequency enhanced encoder backbone with two separate trend and seasonal time–frequency blocks to capture the distinct patterns within the decomposed components in multi-resolutions. Diverse kernel learning strategies of the kernel operations in time–frequency blocks have been explored, by investigating and incorporating the potential different channel-wise correlation patterns of multivariate time series. Experimental evaluation of eight datasets demonstrated that TFDNet is superior to state-of-the-art approaches in both effectiveness and efficiency. The code is available at <span><span>https://github.com/YuxiaoLuo0013/TFDNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"162 ","pages":"Article 111412"},"PeriodicalIF":7.5,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143149860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Image inpainting via Multi-scale Adaptive Priors

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-01-31 DOI: 10.1016/j.patcog.2025.111410

Yufeng Wang , Dongsheng Guo , Haoru Zhao , Min Yang , Haiyong Zheng

{"title":"Image inpainting via Multi-scale Adaptive Priors","authors":"Yufeng Wang , Dongsheng Guo , Haoru Zhao , Min Yang , Haiyong Zheng","doi":"10.1016/j.patcog.2025.111410","DOIUrl":"10.1016/j.patcog.2025.111410","url":null,"abstract":"<div><div>Image inpainting aims to fill the corrupted regions of an image while maintaining global consistency. Many image inpainting methods have made significant progress by incorporating reconstructed single-scale or multi-scale simplified image information as priors to provide explicit structural or textural assistance during the inpainting process. However, these methods typically design priors manually as predefined types of image information based on intuitive choices, overlooking the incomprehensible variations in information that image feature recovery at different scales tends to focus on, which inevitably reduces the efficiency of priors in assisting image feature recovery, and may result in suboptimal performance. To address this issue, we propose Multi-scale Adaptive Priors (MAPs), which dynamically adjust information based on assisting image features at each scale. MAPs are obtained through the MAPs Reconstructor (MAPs-R), which sequentially extracts, reconstructs, and adaptively aggregates multi-scale image representations from corrupted images. To explore MAPs’ potential in assisting inpainting, we designed the MAPs-based Inpainting Network (MAPs-IN), branching feature recovery at each decoder stage to focus on different information levels. Experimental results demonstrate that our proposed priors can more effectively assist in image feature inpainting and ultimately outperform other inpainting methods.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"162 ","pages":"Article 111410"},"PeriodicalIF":7.5,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143297832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Geometric self-supervision for monocular 3D animal pose estimation

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-01-30 DOI: 10.1016/j.patcog.2025.111407

Xiaowei Dai , Shuiwang Li , Qijun Zhao , Hongyu Yang

{"title":"Geometric self-supervision for monocular 3D animal pose estimation","authors":"Xiaowei Dai , Shuiwang Li , Qijun Zhao , Hongyu Yang","doi":"10.1016/j.patcog.2025.111407","DOIUrl":"10.1016/j.patcog.2025.111407","url":null,"abstract":"<div><div>The limited research on 3D animal pose estimation is attributed to data scarcity and perspective ambiguities, despite its significant applications in various fields including biology, medicine, and animation. To resolve data scarcity, we put forward an unsupervised method for estimating 3D animal pose with only 2D pose available alone. To overcome perspective ambiguities, we propose canonical pose, camera, and view consistency losses to represent geometric consistency constraints for self-supervised learning. Specifically, the input 2D pose is fed into the pose generator network and camera network, and then regressed to the 3D canonical pose and camera rotation, respectively. In the training phase, the regressed 3D canonical pose is subjected to random re-projection to synthesize new 2D poses, which are also decomposed into 3D canonical pose and camera rotation to form geometric consistency constraints. Experimental results demonstrate that the proposed method achieves the best performance in unsupervised monocular 3D animal pose estimation. The corresponding code is available at: <span><span>https://github.com/maicao2018/GeoSelfPose</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"162 ","pages":"Article 111407"},"PeriodicalIF":7.5,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143298595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0