IEEE Transactions on Pattern Analysis and Machine Intelligence最新文献_第4页

EasyOutPainter: One Step Image Outpainting with both Continuous Multiple and Resolution. EasyOutPainter：一步图像绘制与连续的多重和分辨率。

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-07-08 DOI: 10.1109/tpami.2025.3586824

Shaofeng Zhang,Qiang Zhou,Zhibin Wang,Hao Li,Junchi Yan

{"title":"EasyOutPainter: One Step Image Outpainting with both Continuous Multiple and Resolution.","authors":"Shaofeng Zhang,Qiang Zhou,Zhibin Wang,Hao Li,Junchi Yan","doi":"10.1109/tpami.2025.3586824","DOIUrl":"https://doi.org/10.1109/tpami.2025.3586824","url":null,"abstract":"Image outpainting aims to generate the content of an input sub-image outside its boundaries, which remains open for existing generative models. This paper explores image outpainting in three directions that have not been achieved in literature to our knowledge: outpainting 1) with continuous multiples (in contrast to the discrete ones by existing methods); 2) with arbitrary resolutions; and 3) in a single step (for any multiples and resolutions). The arbitrary multiple outpainting is achieved by utilizing randomly cropped views from the same image during training to capture arbitrary relative positional information. Specifically, by feeding one view and relative positional embeddings as queries, we can reconstruct another view. At inference, we generate images with arbitrary expansion multiples by inputting an anchor image and its corresponding positional embeddings. The continuous-resolution outpainting is achieved by introducing the multi-scale training strategy into generative models. Specifically, by disentangling the image resolution and the number of patches, it can generate images with arbitrary resolutions without postprocessing. Meanwhile, we propose a query-based contrastive objective to make our method not rely on a pre-trained backbone network which is otherwise often required in peer methods. The comprehensive experimental results on public benchmarks show its superior performance over state-of-the-art approaches.","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"3 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144578805","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

SPOT: Scalable 3D Pre-training via Occupancy Prediction for Learning Transferable 3D Representations. SPOT：可扩展的3D预训练通过占用预测学习可转移的3D表示。

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-07-08 DOI: 10.1109/tpami.2025.3586961

Xiangchao Yan,Runjian Chen,Bo Zhang,Hancheng Ye,Renqiu Xia,Jiakang Yuan,Hongbin Zhou,Xinyu Cai,Botian Shi,Wenqi Shao,Ping Luo,Yu Qiao,Tao Chen,Junchi Yan

{"title":"SPOT: Scalable 3D Pre-training via Occupancy Prediction for Learning Transferable 3D Representations.","authors":"Xiangchao Yan,Runjian Chen,Bo Zhang,Hancheng Ye,Renqiu Xia,Jiakang Yuan,Hongbin Zhou,Xinyu Cai,Botian Shi,Wenqi Shao,Ping Luo,Yu Qiao,Tao Chen,Junchi Yan","doi":"10.1109/tpami.2025.3586961","DOIUrl":"https://doi.org/10.1109/tpami.2025.3586961","url":null,"abstract":"Annotating 3D LiDAR point clouds for perception tasks is fundamental for many applications e.g. autonomous driving, yet it still remains notoriously labor-intensive. Pretraining-finetuning approach can alleviate the labeling burden by fine-tuning a pre-trained backbone across various downstream datasets as well as tasks. In this paper, we propose SPOT, namely Scalable Pre-training via Occupancy prediction for learning Transferable 3D representations under such a label-efficient fine-tuning paradigm. SPOT achieves effectiveness on various public datasets with different downstream tasks, showcasing its general representation power, cross-domain robustness and data scalability which are three key factors for real-world application. Specifically, we both theoretically and empirically show, for the first time, that general representations learning can be achieved through the task of occupancy prediction. Then, to address the domain gap caused by different LiDAR sensors and annotation methods, we develop a beam re-sampling technique for point cloud augmentation combined with class-balancing strategy. Furthermore, scalable pre-training is observed, that is, the downstream performance across all the experiments gets better with more pre-training data. Additionally, such pre-training strategy also remains compatible with unlabeled data. The hope is that our findings will facilitate the understanding of LiDAR points and pave the way for future advancements in LiDAR pre-training.","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"21 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144578807","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

Gradient Projection For Continual Parameter- Efficient Tuning. 梯度投影连续参数-高效调谐。

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-07-08 DOI: 10.1109/tpami.2025.3587032

Jingyang Qiao,Zhizhong Zhang,Xin Tan,Yanyun Qu,Wensheng Zhang,Zhi Han,Yuan Xie

{"title":"Gradient Projection For Continual Parameter- Efficient Tuning.","authors":"Jingyang Qiao,Zhizhong Zhang,Xin Tan,Yanyun Qu,Wensheng Zhang,Zhi Han,Yuan Xie","doi":"10.1109/tpami.2025.3587032","DOIUrl":"https://doi.org/10.1109/tpami.2025.3587032","url":null,"abstract":"Parameter-efficient tunings (PETs) have demonstrated impressive performance and promising perspectives in training large models, while they are still confronted with a common problem: the trade-off between learning new content and protecting old knowledge, leading to zero-shot generalization collapse, and cross-modal hallucination. In this paper, we reformulate Adapter, LoRA, Prefix-tuning, and Prompt-tuning from the perspective of gradient projection, and firstly propose a unified framework called Parameter Efficient Gradient Projection (PEGP). We introduce orthogonal gradient projection into different PET paradigms and theoretically demonstrate that the orthogonal condition for the gradient can effectively resist forgetting even for large-scale models. It therefore modifies the gradient towards the direction that has less impact on the old feature space, with less extra memory space and training time. We extensively evaluate our method with different backbones, including ViT and CLIP, on diverse datasets, and experiments comprehensively demonstrate its efficiency in reducing forgetting in class, online class, domain, task, and multi-modality continual settings.","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"109 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144578806","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

DreamWaltz-G: Expressive 3D Gaussian Avatars from Skeleton-Guided 2D Diffusion DreamWaltz-G：从骨骼引导的2D扩散中表达3D高斯头像

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-07-07 DOI: 10.1109/tpami.2025.3586284

Yukun Huang, Jianan Wang, Ailing Zeng, Zheng-Jun Zha, Lei Zhang, Xihui Liu

引用次数: 0

Unifying Graph Contrastive Learning Via Graph Message Augmentation 基于图信息增强的统一图对比学习

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-07-07 DOI: 10.1109/tpami.2025.3586651

Ziyan Zhang, Bo Jiang, Jin Tang, Bin Luo

引用次数: 0

Learning With Self-Calibrator for Fast and Robust Low-Light Image Enhancement 用自校正器学习快速鲁棒弱光图像增强

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-07-07 DOI: 10.1109/tpami.2025.3586712

Long Ma, Tengyu Ma, Chengpei Xu, Jinyuan Liu, Xin Fan, Zhongxuan Luo, Risheng Liu

引用次数: 0

Efficient Distortion-minimized Layerwise Pruning 有效的扭曲最小化分层修剪

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-07-07 DOI: 10.1109/tpami.2025.3586418

Kaixin Xu, Zhe Wang, Runtao Huang, Xue Geng, Jie Lin, Xulei Yang, Min Wu, Xiaoli Li, Weisi Lin

引用次数: 0

Event-based Photometric Bundle Adjustment 基于事件的光度束调整

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-07-07 DOI: 10.1109/tpami.2025.3586497

Shuang Guo, Guillermo Gallego

引用次数: 0

BiVM: Accurate Binarized Neural Network for Efficient Video Matting 用于高效视频抠图的精确二值化神经网络

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-07-02 DOI: 10.1109/tpami.2025.3584928

Haotong Qin, Xianglong Liu, Xudong Ma, Lei Ke, Yulun Zhang, Jie Luo, Michele Magno

引用次数: 0

Kernelized Hypergraph Neural Networks 核化超图神经网络

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-07-02 DOI: 10.1109/tpami.2025.3585179

Yifan Feng, Yifan Zhang, Shihui Ying, Shaoyi Du, Yue Gao

引用次数: 0