IEEE transactions on pattern analysis and machine intelligence最新文献_第2页

Systematic Bias of Machine Learning Regression Models and Correction. 机器学习回归模型的系统偏差及其校正。

IEEE transactions on pattern analysis and machine intelligence Pub Date : 2025-03-18 DOI: 10.1109/TPAMI.2025.3552368

Hwiyoung Lee, Shuo Chen

{"title":"Systematic Bias of Machine Learning Regression Models and Correction.","authors":"Hwiyoung Lee, Shuo Chen","doi":"10.1109/TPAMI.2025.3552368","DOIUrl":"10.1109/TPAMI.2025.3552368","url":null,"abstract":"Machine learning models for continuous outcomes often yield systematically biased predictions, particularly for values that largely deviate from the mean. Specifically, predictions for large-valued outcomes tend to be negatively biased (underestimating actual values), while those for small-valued outcomes are positively biased (overestimating actual values). We refer to this linear central tendency warped bias as the \"systematic bias of machine learning regression\". In this paper, we first demonstrate that this systematic prediction bias persists across various machine learning regression models, and then delve into its theoretical underpinnings. To address this issue, we propose a general constrained optimization approach designed to correct this bias and develop computationally efficient implementation algorithms. Simulation results indicate that our correction method effectively eliminates the bias from the predicted outcomes. We apply the proposed approach to the prediction of brain age using neuroimaging data. In comparison to competing machine learning regression models, our method effectively addresses the longstanding issue of \"systematic bias of machine learning regression\" in neuroimaging-based brain age calculation, yielding unbiased predictions of brain age.","PeriodicalId":94034,"journal":{"name":"IEEE transactions on pattern analysis and machine intelligence","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143660149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Conditional Cauchy-Schwarz Divergence With Applications to Time-Series Data and Sequential Decision Making. 条件Cauchy-Schwarz散度在时间序列数据和顺序决策中的应用。

IEEE transactions on pattern analysis and machine intelligence Pub Date : 2025-03-18 DOI: 10.1109/TPAMI.2025.3552434

Shujian Yu, Hongming Li, Sigurd Lokse, Robert Jenssen, Jose C Principe

引用次数: 0

Hard-aware Instance Adaptive Self-training for Unsupervised Cross-domain Semantic Segmentation. 无监督跨域语义分割的硬感知实例自适应训练。

IEEE transactions on pattern analysis and machine intelligence Pub Date : 2025-03-18 DOI: 10.1109/TPAMI.2025.3552484

Chuang Zhu, Kebin Liu, Wenqi Tang, Ke Mei, Jiaqi Zou, Tiejun Huang

{"title":"Hard-aware Instance Adaptive Self-training for Unsupervised Cross-domain Semantic Segmentation.","authors":"Chuang Zhu, Kebin Liu, Wenqi Tang, Ke Mei, Jiaqi Zou, Tiejun Huang","doi":"10.1109/TPAMI.2025.3552484","DOIUrl":"10.1109/TPAMI.2025.3552484","url":null,"abstract":"The divergence between labeled training data and unlabeled testing data is a significant challenge for recent deep learning models. Unsupervised domain adaptation (UDA) attempts to solve such problem. Recent works show that self-training is a powerful approach to UDA. However, existing methods have difficulty in balancing the scalability and performance. In this paper, we propose a hard-aware instance adaptive self-training framework for UDA on the task of semantic segmentation. To effectively improve the quality and diversity of pseudo-labels, we develop a novel pseudo-label generation strategy with an instance adaptive selector. We further enrich the hard class pseudo-labels with inter-image information through a skillfully designed hard-aware pseudo-label augmentation. Besides, we propose the region-adaptive regularization to smooth the pseudo-label region and sharpen the non-pseudo-label region. For the non-pseudo-label region, consistency constraint is also constructed to introduce stronger supervision signals during model optimization. Our method is so concise and efficient that it is easy to be generalized to other UDA methods. Experiments on GTA5 Cityscapes, SYNTHIA Cityscapes, and Cityscapes Oxford RobotCar demonstrate the superior performance of our approach compared with the state-of-the-art methods. Our codes are available at https://github.com/bupt-ai-cz/HIAST.","PeriodicalId":94034,"journal":{"name":"IEEE transactions on pattern analysis and machine intelligence","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143660109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation. Diff9D：基于扩散的域广义类别级9-DoF目标姿态估计。

IEEE transactions on pattern analysis and machine intelligence Pub Date : 2025-03-18 DOI: 10.1109/TPAMI.2025.3552132

Jian Liu, Wei Sun, Hui Yang, Pengchao Deng, Chongpei Liu, Nicu Sebe, Hossein Rahmani, Ajmal Mian

{"title":"Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation.","authors":"Jian Liu, Wei Sun, Hui Yang, Pengchao Deng, Chongpei Liu, Nicu Sebe, Hossein Rahmani, Ajmal Mian","doi":"10.1109/TPAMI.2025.3552132","DOIUrl":"https://doi.org/10.1109/TPAMI.2025.3552132","url":null,"abstract":"Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance. Our code will be made public at https://github.com/CNJianLiu/Diff9D.","PeriodicalId":94034,"journal":{"name":"IEEE transactions on pattern analysis and machine intelligence","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143660098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Human as Points: Explicit Point-based 3D Human Reconstruction from Single-view RGB Images. 人作为点：从单视图RGB图像中明确的基于点的3D人体重建。

IEEE transactions on pattern analysis and machine intelligence Pub Date : 2025-03-18 DOI: 10.1109/TPAMI.2025.3552408

Yingzhi Tang, Qijian Zhang, Yebin Liu, Junhui Hou

{"title":"Human as Points: Explicit Point-based 3D Human Reconstruction from Single-view RGB Images.","authors":"Yingzhi Tang, Qijian Zhang, Yebin Liu, Junhui Hou","doi":"10.1109/TPAMI.2025.3552408","DOIUrl":"10.1109/TPAMI.2025.3552408","url":null,"abstract":"The latest trends in the research field of single-view human reconstruction are devoted to learning deep implicit functions constrained by explicit body shape priors. Despite the remarkable performance improvements compared with traditional processing pipelines, existing learning approaches still exhibit limitations in terms of flexibility, generalizability, robustness, and/or representation capability. To comprehensively address the above issues, in this paper, we investigate an explicit point-based human reconstruction framework named HaP, which utilizes point clouds as the intermediate representation of the target geometric structure. Technically, our approach features fully explicit point cloud estimation (exploiting depth and SMPL), manipulation (SMPL rectification), generation (built upon diffusion), and refinement (displacement learning and depth replacement) in the 3D geometric space, instead of an implicit learning process that can be ambiguous and less controllable. Extensive experiments demonstrate that our framework achieves quantitative performance improvements of 20% to 40% over current state-of-the-art methods, and better qualitative results. Our promising results may indicate a paradigm rollback to the fully-explicit and geometry-centric algorithm design. In addition, we newly contribute a real-scanned 3D human dataset featuring more intricate geometric details. We will make our code and data publicly available at https://github.com/yztang4/HaP.","PeriodicalId":94034,"journal":{"name":"IEEE transactions on pattern analysis and machine intelligence","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143660143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Revisiting Stochastic Multi-Level Compositional Optimization. 再论随机多级成分优化。

IEEE transactions on pattern analysis and machine intelligence Pub Date : 2025-03-18 DOI: 10.1109/TPAMI.2025.3552197

Wei Jiang, Sifan Yang, Yibo Wang, Tianbao Yang, Lijun Zhang

{"title":"Revisiting Stochastic Multi-Level Compositional Optimization.","authors":"Wei Jiang, Sifan Yang, Yibo Wang, Tianbao Yang, Lijun Zhang","doi":"10.1109/TPAMI.2025.3552197","DOIUrl":"https://doi.org/10.1109/TPAMI.2025.3552197","url":null,"abstract":"This paper explores stochastic multi-level compositional optimization, where the objective function is a composition of multiple smooth functions. Traditional methods for solving this problem suffer from either sub-optimal sample complexities or require huge batch sizes. To address these limitations, we introduce the Stochastic Multi-level Variance Reduction (SMVR) method. In the expectation case, our SMVR method attains the optimal sample complexity of to find an -stationary point for non-convex objectives. When the function satisfies convexity or the Polyak-Łojasiewicz (PL) condition, we propose a stage-wise SMVR variant. This variant improves the sample complexity to for convex functions and for functions meeting the -PL condition or -strong convexity. These complexities match the lower bounds not only in terms of but also in terms of (for PL or strongly convex functions), without relying on large batch sizes in each iteration. Furthermore, in the finite-sum case, we develop the SMVR-FS algorithm, which can achieve a complexity of for non-convex objectives, for convex functions and for objectives satisfying the -PL condition, where denotes the number of functions in each level. To make use of adaptive learning rates, we propose the Adaptive SMVR method, which maintains the same complexities while demonstrating faster convergence in practice.","PeriodicalId":94034,"journal":{"name":"IEEE transactions on pattern analysis and machine intelligence","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143660145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Instant Gaussian Splatting Generation for High-Quality and Real-Time Facial Asset Rendering. 用于高质量实时面部资产渲染的即时高斯溅射生成技术

IEEE transactions on pattern analysis and machine intelligence Pub Date : 2025-03-14 DOI: 10.1109/TPAMI.2025.3550195

Dafei Qin, Hongyang Lin, Qixuan Zhang, Kaichun Qiao, Longwen Zhang, Jun Saito, Zijun Zhao, Jingyi Yu, Lan Xu, Taku Komura

{"title":"Instant Gaussian Splatting Generation for High-Quality and Real-Time Facial Asset Rendering.","authors":"Dafei Qin, Hongyang Lin, Qixuan Zhang, Kaichun Qiao, Longwen Zhang, Jun Saito, Zijun Zhao, Jingyi Yu, Lan Xu, Taku Komura","doi":"10.1109/TPAMI.2025.3550195","DOIUrl":"10.1109/TPAMI.2025.3550195","url":null,"abstract":"Traditional and AI-driven modeling techniques enable high-fidelity 3D asset generation from scans, videos, or text prompts. However, editing and rendering these assets often involves a trade-off between quality and speed. In this paper, we propose GauFace, a novel Gaussian Splatting representation, tailored for efficient rendering of facial mesh with textures. Then, we introduce TransGS, a diffusion transformer that instantly generates the GauFace assets from mesh, textures and lightning conditions. Specifically, we adopt a patch-based pipeline to handle the vast number of Gaussian Points, a novel texel-aligned sampling scheme with UV positional encoding to enhance the throughput of generating GauFace assets. Once trained, TransGS can generate GauFace assets in 5 seconds, delivering high fidelity and real-time facial interaction of 30fps@1440p to a Snapdragon 8 Gen 2 mobile platform. The rich conditional modalities further enable editing and animation capabilities reminiscent of traditional CG pipelines. We conduct extensive evaluations and user studies, compared to traditional renderers, as well as recent neural rendering methods. They demonstrate the superior performance of our approach for facial asset rendering. We also showcase diverse applications of facial assets using our TransGS approach and GauFace representation, across various platforms like PCs, phones, and VR headsets.","PeriodicalId":94034,"journal":{"name":"IEEE transactions on pattern analysis and machine intelligence","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143631139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deformable Graph Transformer. 可变形的图形转换器。

IEEE transactions on pattern analysis and machine intelligence Pub Date : 2025-03-14 DOI: 10.1109/TPAMI.2025.3550281

Jinyoung Park, Seongjun Yun, Hyeonjin Park, Jaewoo Kang, Jisu Jeong, Kyung-Min Kim, Jung-Woo Ha, Hyunwoo J Kim

{"title":"Deformable Graph Transformer.","authors":"Jinyoung Park, Seongjun Yun, Hyeonjin Park, Jaewoo Kang, Jisu Jeong, Kyung-Min Kim, Jung-Woo Ha, Hyunwoo J Kim","doi":"10.1109/TPAMI.2025.3550281","DOIUrl":"https://doi.org/10.1109/TPAMI.2025.3550281","url":null,"abstract":"Transformer-based models have recently shown success in representation learning on graph-structured data beyond natural language processing and computer vision. However, the success is limited to small-scale graphs due to the drawbacks of full dot-product attention on graphs such as the quadratic complexity with respect to the number of nodes and message aggregation from enormous irrelevant nodes. To address these issues, we propose Deformable Graph Transformer (DGT) that performs sparse attention via dynamically selected relevant nodes for efficiently handling large-scale graphs with a linear complexity in the number of nodes. Specifically, our framework first constructs multiple node sequences with various criteria to consider both structural and semantic proximity. Then, combining with our learnable Katz Positional Encodings, the sparse attention is applied to the node sequences for learning node representations with a significantly reduced computational cost. Extensive experiments demonstrate that our DGT achieves superior performance on 7 graph benchmark datasets with 2.5 ∼ 449 times less computational cost compared to transformer-based graph models with full attention.","PeriodicalId":94034,"journal":{"name":"IEEE transactions on pattern analysis and machine intelligence","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143631138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MB-RACS: Measurement-Bounds-based Rate-Adaptive Image Compressed Sensing Network. MB-RACS：基于测量边界的速率自适应图像压缩传感网络

IEEE transactions on pattern analysis and machine intelligence Pub Date : 2025-03-10 DOI: 10.1109/TPAMI.2025.3549986

Yujun Huang, Bin Chen, Naiqi Li, Baoyi An, Shu-Tao Xia, Yaowei Wang

{"title":"MB-RACS: Measurement-Bounds-based Rate-Adaptive Image Compressed Sensing Network.","authors":"Yujun Huang, Bin Chen, Naiqi Li, Baoyi An, Shu-Tao Xia, Yaowei Wang","doi":"10.1109/TPAMI.2025.3549986","DOIUrl":"10.1109/TPAMI.2025.3549986","url":null,"abstract":"Conventional compressed sensing (CS) algorithms typically apply a uniform sampling rate to different image blocks. A more strategic approach could be to allocate the number of measurements adaptively, based on each image block's complexity. In this paper, we propose a Measurement-Bounds-based Rate-Adaptive Image Compressed Sensing Network (MB-RACS) framework, which aims to adaptively determine the sampling rate for each image block in accordance with traditional measurement bounds theory. Moreover, since in real-world scenarios statistical information about the original image cannot be directly obtained, we suggest a multi-stage rate-adaptive sampling strategy. This strategy sequentially adjusts the sampling ratio allocation based on the information gathered from previous samplings. We formulate the multi-stage rate-adaptive sampling as a convex optimization problem and address it using a combination of Newton's method and binary search techniques. Our experiments demonstrate that the proposed MB-RACS method surpasses current leading methods, with experimental evidence also underscoring the effectiveness of each module within our proposed framework.","PeriodicalId":94034,"journal":{"name":"IEEE transactions on pattern analysis and machine intelligence","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143598606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE transactions on pattern analysis and machine intelligence Pub Date : 2025-03-10 DOI: 10.1109/TPAMI.2025.3550032

Hafsa Ennajari, Nizar Bouguila, Jamal Bentahar

引用次数: 0