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

Biarchetype Analysis: Simultaneous Learning of Observations and Features Based on Extremes. 双车型分析：基于极值同时学习观察结果和特征

IEEE transactions on pattern analysis and machine intelligence Pub Date : 2024-12-01 Epub Date: 2024-11-06 DOI: 10.1109/TPAMI.2024.3400730

Aleix Alcacer, Irene Epifanio, Ximo Gual-Arnau

引用次数: 0

HC ²L: Hybrid and Cooperative Contrastive Learning for Cross-Lingual Spoken Language Understanding. HC2L：用于跨语言口语理解的混合合作对比学习。

IEEE transactions on pattern analysis and machine intelligence Pub Date : 2024-12-01 Epub Date: 2024-11-06 DOI: 10.1109/TPAMI.2024.3402746

Bowen Xing, Ivor W Tsang

{"title":"HC 2L: Hybrid and Cooperative Contrastive Learning for Cross-Lingual Spoken Language Understanding.","authors":"Bowen Xing, Ivor W Tsang","doi":"10.1109/TPAMI.2024.3402746","DOIUrl":"10.1109/TPAMI.2024.3402746","url":null,"abstract":"State-of-the-art model for zero-shot cross-lingual spoken language understanding performs cross-lingual unsupervised contrastive learning to achieve the label-agnostic semantic alignment between each utterance and its code-switched data. However, it ignores the precious intent/slot labels, whose label information is promising to help capture the label-aware semantics structure and then leverage supervised contrastive learning to improve both source and target languages' semantics. In this paper, we propose Hybrid and Cooperative Contrastive Learning to address this problem. Apart from cross-lingual unsupervised contrastive learning, we design a holistic approach that exploits source language supervised contrastive learning, cross-lingual supervised contrastive learning and multilingual supervised contrastive learning to perform label-aware semantics alignments in a comprehensive manner. Each kind of supervised contrastive learning mechanism includes both single-task and joint-task scenarios. In our model, one contrastive learning mechanism's input is enhanced by others. Thus the total four contrastive learning mechanisms are cooperative to learn more consistent and discriminative representations in the virtuous cycle during the training process. Experiments show that our model obtains consistent improvements over 9 languages, achieving new state-of-the-art performance.","PeriodicalId":94034,"journal":{"name":"IEEE transactions on pattern analysis and machine intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141072477","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

Interaction-Based Inductive Bias in Graph Neural Networks: Enhancing Protein-Ligand Binding Affinity Predictions From 3D Structures. 图神经网络中基于相互作用的归纳偏差：从三维结构增强蛋白质配体结合亲和力预测。

IEEE transactions on pattern analysis and machine intelligence Pub Date : 2024-12-01 Epub Date: 2024-11-06 DOI: 10.1109/TPAMI.2024.3400515

Ziduo Yang, Weihe Zhong, Qiujie Lv, Tiejun Dong, Guanxing Chen, Calvin Yu-Chian Chen

{"title":"Interaction-Based Inductive Bias in Graph Neural Networks: Enhancing Protein-Ligand Binding Affinity Predictions From 3D Structures.","authors":"Ziduo Yang, Weihe Zhong, Qiujie Lv, Tiejun Dong, Guanxing Chen, Calvin Yu-Chian Chen","doi":"10.1109/TPAMI.2024.3400515","DOIUrl":"10.1109/TPAMI.2024.3400515","url":null,"abstract":"Inductive bias in machine learning (ML) is the set of assumptions describing how a model makes predictions. Different ML-based methods for protein-ligand binding affinity (PLA) prediction have different inductive biases, leading to different levels of generalization capability and interpretability. Intuitively, the inductive bias of an ML-based model for PLA prediction should fit in with biological mechanisms relevant for binding to achieve good predictions with meaningful reasons. To this end, we propose an interaction-based inductive bias to restrict neural networks to functions relevant for binding with two assumptions: 1) A protein-ligand complex can be naturally expressed as a heterogeneous graph with covalent and non-covalent interactions; 2) The predicted PLA is the sum of pairwise atom-atom affinities determined by non-covalent interactions. The interaction-based inductive bias is embodied by an explainable heterogeneous interaction graph neural network (EHIGN) for explicitly modeling pairwise atom-atom interactions to predict PLA from 3D structures. Extensive experiments demonstrate that EHIGN achieves better generalization capability than other state-of-the-art ML-based baselines in PLA prediction and structure-based virtual screening. More importantly, comprehensive analyses of distance-affinity, pose-affinity, and substructure-affinity relations suggest that the interaction-based inductive bias can guide the model to learn atomic interactions that are consistent with physical reality. As a case study to demonstrate practical usefulness, our method is tested for predicting the efficacy of Nirmatrelvir against SARS-CoV-2 variants. EHIGN successfully recognizes the changes in the efficacy of Nirmatrelvir for different SARS-CoV-2 variants with meaningful reasons.","PeriodicalId":94034,"journal":{"name":"IEEE transactions on pattern analysis and machine intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140917578","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

EBMGC-GNF: Efficient Balanced Multi-View Graph Clustering via Good Neighbor Fusion. EBMGC-GNF：通过好邻居融合实现高效平衡多视图图聚类。

IEEE transactions on pattern analysis and machine intelligence Pub Date : 2024-12-01 Epub Date: 2024-11-06 DOI: 10.1109/TPAMI.2024.3398220

Danyang Wu, Zhenkun Yang, Jitao Lu, Jin Xu, Xiangmin Xu, Feiping Nie

引用次数: 0

Evaluation Metrics for Intelligent Generation of Graphical Game Assets: A Systematic Survey-Based Framework. 智能生成图形游戏资产的评估指标：基于系统调查的框架。

IEEE transactions on pattern analysis and machine intelligence Pub Date : 2024-12-01 Epub Date: 2024-11-06 DOI: 10.1109/TPAMI.2024.3398998

Kaisei Fukaya, Damon Daylamani-Zad, Harry Agius

引用次数: 0

A Comprehensive Review of Image Line Segment Detection and Description: Taxonomies, Comparisons, and Challenges. 图像线段检测与描述综合评述：分类、比较与挑战

IEEE transactions on pattern analysis and machine intelligence Pub Date : 2024-12-01 Epub Date: 2024-11-06 DOI: 10.1109/TPAMI.2024.3400881

Xinyu Lin, Yingjie Zhou, Yipeng Liu, Ce Zhu

{"title":"A Comprehensive Review of Image Line Segment Detection and Description: Taxonomies, Comparisons, and Challenges.","authors":"Xinyu Lin, Yingjie Zhou, Yipeng Liu, Ce Zhu","doi":"10.1109/TPAMI.2024.3400881","DOIUrl":"10.1109/TPAMI.2024.3400881","url":null,"abstract":"An image line segment is a fundamental low-level visual feature that delineates straight, slender, and uninterrupted portions of objects and scenarios within images. Detection and description of line segments lay the basis for numerous vision tasks. Although many studies have aimed to detect and describe line segments, a comprehensive review is lacking, obstructing their progress. This study fills the gap by comprehensively reviewing related studies on detecting and describing two-dimensional image line segments to provide researchers with an overall picture and deep understanding. Based on their mechanisms, two taxonomies for line segment detection and description are presented to introduce, analyze, and summarize these studies, facilitating researchers to learn about them quickly and extensively. The key issues, core ideas, advantages and disadvantages of existing methods, and their potential applications for each category are analyzed and summarized, including previously unknown findings. The challenges in existing methods and corresponding insights for potentially solving them are also provided to inspire researchers. In addition, some state-of-the-art line segment detection and description algorithms are evaluated without bias, and the evaluation code will be publicly available. The theoretical analysis, coupled with the experimental results, can guide researchers in selecting the best method for their intended vision applications. Finally, this study provides insights for potentially interesting future research directions to attract more attention from researchers to this field.","PeriodicalId":94034,"journal":{"name":"IEEE transactions on pattern analysis and machine intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140924112","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

CrossZoom: Simultaneous Motion Deblurring and Event Super-Resolving. CrossZoom：同时进行运动去模糊和事件超解像。

IEEE transactions on pattern analysis and machine intelligence Pub Date : 2024-12-01 Epub Date: 2024-11-06 DOI: 10.1109/TPAMI.2024.3402972

Chi Zhang, Xiang Zhang, Mingyuan Lin, Cheng Li, Chu He, Wen Yang, Gui-Song Xia, Lei Yu

{"title":"CrossZoom: Simultaneous Motion Deblurring and Event Super-Resolving.","authors":"Chi Zhang, Xiang Zhang, Mingyuan Lin, Cheng Li, Chu He, Wen Yang, Gui-Song Xia, Lei Yu","doi":"10.1109/TPAMI.2024.3402972","DOIUrl":"10.1109/TPAMI.2024.3402972","url":null,"abstract":"Even though the collaboration between traditional and neuromorphic event cameras brings prosperity to frame-event based vision applications, the performance is still confined by the resolution gap crossing two modalities in both spatial and temporal domains. This paper is devoted to bridging the gap by increasing the temporal resolution for images, i.e., motion deblurring, and the spatial resolution for events, i.e., event super-resolving, respectively. To this end, we introduce CrossZoom, a novel unified neural Network (CZ-Net) to jointly recover sharp latent sequences within the exposure period of a blurry input and the corresponding High-Resolution (HR) events. Specifically, we present a multi-scale blur-event fusion architecture that leverages the scale-variant properties and effectively fuses cross-modal information to achieve cross-enhancement. Attention-based adaptive enhancement and cross-interaction prediction modules are devised to alleviate the distortions inherent in Low-Resolution (LR) events and enhance the final results through the prior blur-event complementary information. Furthermore, we propose a new dataset containing HR sharp-blurry images and the corresponding HR-LR event streams to facilitate future research. Extensive qualitative and quantitative experiments on synthetic and real-world datasets demonstrate the effectiveness and robustness of the proposed method.","PeriodicalId":94034,"journal":{"name":"IEEE transactions on pattern analysis and machine intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141072476","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

Triplet Adaptation Framework for Robust Semi-Supervised Learning. 鲁棒性半监督学习的三重适应框架

IEEE transactions on pattern analysis and machine intelligence Pub Date : 2024-12-01 Epub Date: 2024-11-06 DOI: 10.1109/TPAMI.2024.3404450

Ruibing Hou, Hong Chang, Bingpeng Ma, Shiguang Shan, Xilin Chen

{"title":"Triplet Adaptation Framework for Robust Semi-Supervised Learning.","authors":"Ruibing Hou, Hong Chang, Bingpeng Ma, Shiguang Shan, Xilin Chen","doi":"10.1109/TPAMI.2024.3404450","DOIUrl":"10.1109/TPAMI.2024.3404450","url":null,"abstract":"Semi-supervised learning (SSL) suffers from severe performance degradation when labeled and unlabeled data come from inconsistent and imbalanced distribution. Nonetheless, there is a lack of theoretical guidance regarding a remedy for this issue. To bridge the gap between theoretical insights and practical solutions, we embark to an analysis of generalization bound of classic SSL algorithms. This analysis reveals that distribution inconsistency between unlabeled and labeled data can cause a significant generalization error bound. Motivated by this theoretical insight, we present a Triplet Adaptation Framework (TAF) to reduce the distribution divergence and improve the generalization of SSL models. TAF comprises three adapters: Balanced Residual Adapter, aiming to map the class distribution of labeled and unlabeled data to a uniform distribution for reducing class distribution divergence; Representation Adapter, aiming to map the representation distribution of unlabeled data to labeled one for reducing representation distribution divergence; and Pseudo-Label Adapter, aiming to align the predicted pseudo-labels with the class distribution of unlabeled data, thereby preventing erroneous pseudo-labels from exacerbating representation divergence. These three adapters collaborate synergistically to reduce the generalization bound, ultimately achieving a more robust and generalizable SSL model. Extensive experiments across various robust SSL scenarios validate the efficacy of our method.","PeriodicalId":94034,"journal":{"name":"IEEE transactions on pattern analysis and machine intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141181735","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

Motion-Aware Dynamic Graph Neural Network for Video Compressive Sensing. 用于视频压缩传感的运动感知动态图神经网络

IEEE transactions on pattern analysis and machine intelligence Pub Date : 2024-12-01 Epub Date: 2024-11-06 DOI: 10.1109/TPAMI.2024.3395804

Ruiying Lu, Ziheng Cheng, Bo Chen, Xin Yuan

{"title":"Motion-Aware Dynamic Graph Neural Network for Video Compressive Sensing.","authors":"Ruiying Lu, Ziheng Cheng, Bo Chen, Xin Yuan","doi":"10.1109/TPAMI.2024.3395804","DOIUrl":"10.1109/TPAMI.2024.3395804","url":null,"abstract":"Video snapshot compressive imaging (SCI) utilizes a 2D detector to capture sequential video frames and compress them into a single measurement. Various reconstruction methods have been developed to recover the high-speed video frames from the snapshot measurement. However, most existing reconstruction methods are incapable of efficiently capturing long-range spatial and temporal dependencies, which are critical for video processing. In this paper, we propose a flexible and robust approach based on the graph neural network (GNN) to efficiently model non-local interactions between pixels in space and time regardless of the distance. Specifically, we develop a motion-aware dynamic GNN for better video representation, i.e., represent each node as the aggregation of relative neighbors under the guidance of frame-by-frame motions, which consists of motion-aware dynamic sampling, cross-scale node sampling, global knowledge integration, and graph aggregation. Extensive results on both simulation and real data demonstrate both the effectiveness and efficiency of the proposed approach, and the visualization illustrates the intrinsic dynamic sampling operations of our proposed model for boosting the video SCI reconstruction results. The code and model will be released.","PeriodicalId":94034,"journal":{"name":"IEEE transactions on pattern analysis and machine intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140893092","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

Deterministic Gradient-Descent Learning of Linear Regressions: Adaptive Algorithms, Convergence Analysis and Noise Compensation. 线性回归的确定性梯度-后裔学习：自适应算法、收敛分析和噪声补偿。

IEEE transactions on pattern analysis and machine intelligence Pub Date : 2024-12-01 Epub Date: 2024-11-06 DOI: 10.1109/TPAMI.2024.3399312

Kang-Zhi Liu, Chao Gan

{"title":"Deterministic Gradient-Descent Learning of Linear Regressions: Adaptive Algorithms, Convergence Analysis and Noise Compensation.","authors":"Kang-Zhi Liu, Chao Gan","doi":"10.1109/TPAMI.2024.3399312","DOIUrl":"10.1109/TPAMI.2024.3399312","url":null,"abstract":"Weight learning forms a basis for the machine learning and numerous algorithms have been adopted up to date. Most of the algorithms were either developed in the stochastic framework or aimed at minimization of loss or regret functions. Asymptotic convergence of weight learning, vital for good output prediction, was seldom guaranteed for online applications. Since linear regression is the most fundamental component in machine learning, we focus on this model in this paper. Aiming at online applications, a deterministic analysis method is developed based on LaSalle's invariance principle. Convergence conditions are derived for both the first-order and the second-order learning algorithms, without resorting to any stochastic argument. Moreover, the deterministic approach makes it easy to analyze the noise influence. Specifically, adaptive hyperparameters are derived in this framework and their tuning rules disclosed for the compensation of measurement noise. Comparison with four most popular algorithms validates that this approach has a higher learning capability and is quite promising in enhancing the weight learning performance.","PeriodicalId":94034,"journal":{"name":"IEEE transactions on pattern analysis and machine intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140905054","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