IEEE Transactions on Pattern Analysis and Machine Intelligence最新文献_第7页

On-the-fly Modulation for Balanced Multimodal Learning 实时调制，实现多模态均衡学习

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2024-09-25 DOI: 10.1109/tpami.2024.3468315

Yake Wei, Di Hu, Henghui Du, Ji-Rong Wen

引用次数: 0

Developmental Plasticity-inspired Adaptive Pruning for Deep Spiking and Artificial Neural Networks 深度尖峰和人工神经网络的发育可塑性启发自适应修剪

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2024-09-24 DOI: 10.1109/tpami.2024.3467268

Bing Han, Feifei Zhao, Yi Zeng, Guobin Shen

引用次数: 0

High-performance real-world optical computing trained by in situ gradient-based model-free optimization 通过基于梯度的原位无模型优化训练高性能真实世界光学计算

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2024-09-24 DOI: 10.1109/tpami.2024.3466853

Guangyuan Zhao, Xin Shu, Renjie Zhou

引用次数: 0

Unsupervised Dual Deep Hashing with Semantic-Index and Content-Code for Cross-Modal Retrieval 利用语义索引和内容代码的无监督双深度散列技术实现跨模态检索

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2024-09-24 DOI: 10.1109/tpami.2024.3467130

Bin Zhang, Yue Zhang, Junyu Li, Jiazhou Chen, Tatsuya Akutsu, Yiu-ming Cheung, Hongmin Cai

引用次数: 0

Weakly Supervised Monocular 3D Object Detection by Spatial-Temporal View Consistency 通过时空视图一致性进行弱监督单目三维物体检测

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2024-09-24 DOI: 10.1109/tpami.2024.3466915

Wencheng Han, Runzhou Tao, Haibin Ling, Jianbing Shen

引用次数: 0

Multi-sensor Learning Enables Information Transfer across Different Sensory Data and Augments Multi-modality Imaging. 多传感器学习实现了不同感官数据之间的信息传递，并增强了多模态成像能力。

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2024-09-20 DOI: 10.1109/tpami.2024.3465649

Lingting Zhu,Yizheng Chen,Lianli Liu,Lei Xing,Lequan Yu

{"title":"Multi-sensor Learning Enables Information Transfer across Different Sensory Data and Augments Multi-modality Imaging.","authors":"Lingting Zhu,Yizheng Chen,Lianli Liu,Lei Xing,Lequan Yu","doi":"10.1109/tpami.2024.3465649","DOIUrl":"https://doi.org/10.1109/tpami.2024.3465649","url":null,"abstract":"Multi-modality imaging is widely used in clinical practice and biomedical research to gain a comprehensive understanding of an imaging subject. Currently, multi-modality imaging is accomplished by post hoc fusion of independently reconstructed images under the guidance of mutual information or spatially registered hardware, which limits the accuracy and utility of multi-modality imaging. Here, we investigate a data-driven multi-modality imaging (DMI) strategy for synergetic imaging of CT and MRI. We reveal two distinct types of features in multi-modality imaging, namely intra- and inter-modality features, and present a multi-sensor learning (MSL) framework to utilize the crossover inter-modality features for augmented multi-modality imaging. The MSL imaging approach breaks down the boundaries of traditional imaging modalities and allows for optimal hybridization of CT and MRI, which maximizes the use of sensory data. We showcase the effectiveness of our DMI strategy through synergetic CT-MRI brain imaging. The principle of DMI is quite general and holds enormous potential for various DMI applications across disciplines.","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"36 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142275199","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

Event-enhanced Snapshot Mosaic Hyperspectral Frame Deblurring. 事件增强快照马赛克高光谱帧去模糊。

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2024-09-20 DOI: 10.1109/tpami.2024.3465455

Mengyue Geng,Lizhi Wang,Lin Zhu,Wei Zhang,Ruiqin Xiong,Yonghong Tian

{"title":"Event-enhanced Snapshot Mosaic Hyperspectral Frame Deblurring.","authors":"Mengyue Geng,Lizhi Wang,Lin Zhu,Wei Zhang,Ruiqin Xiong,Yonghong Tian","doi":"10.1109/tpami.2024.3465455","DOIUrl":"https://doi.org/10.1109/tpami.2024.3465455","url":null,"abstract":"Snapshot Mosaic Hyperspectral Cameras (SMHCs) are popular hyperspectral imaging devices for acquiring both color and motion details of scenes. However, the narrow-band spectral filters in SMHCs may negatively impact their motion perception ability, resulting in blurry SMHC frames. In this paper, we propose a hardware-software collaborative approach to address the blurring issue of SMHCs. Our approach involves integrating SMHCs with neuromorphic event cameras for efficient event-enhanced SMHC frame deblurring. To achieve spectral information recovery guided by event signals, we formulate a spectral-aware Event-based Double Integral (sEDI) model that links SMHC frames and events from a spectral perspective, providing principled model design insights. Then, we develop a Diffusion-guided Noise Awareness (DNA) training framework that utilizes diffusion models to learn noise-aware features and promote model robustness towards camera noise. Furthermore, we design an Event-enhanced Hyperspectral frame Deblurring Network (EvHDNet) based on sEDI, which is trained with DNA and features improved spatial-spectral learning and modality interaction for reliable SMHC frame deblurring. Experiments on both synthetic data and real data show that the proposed DNA + EvHDNet outperforms stateof-the-art methods on both spatial and spectral fidelity. The code and dataset will be made publicly available.","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"21 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142275196","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

RoBoSS: A Robust, Bounded, Sparse, and Smooth Loss Function for Supervised Learning RoBoSS：用于监督学习的鲁棒、有界、稀疏且平滑的损失函数

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2024-09-20 DOI: 10.1109/tpami.2024.3465535

Mushir Akhtar, M. Tanveer, Mohd. Arshad

引用次数: 0

Unveiling the Power of Self-Supervision for Multi-View Multi-Human Association and Tracking 为多视角多人类关联和跟踪揭示自我监督的力量

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2024-09-19 DOI: 10.1109/tpami.2024.3463966

Wei Feng, Feifan Wang, Ruize Han, Yiyang Gan, Zekun Qian, Junhui Hou, Song Wang

引用次数: 0

Revisiting Nonlocal Self-Similarity from Continuous Representation 从连续表征再看非局部自相似性

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2024-09-19 DOI: 10.1109/tpami.2024.3464875

Yisi Luo, Xile Zhao, Deyu Meng

引用次数: 0