IEEE Transactions on Pattern Analysis and Machine Intelligence最新文献_第2页

Systematic Investigation of Sparse Perturbed Sharpness-Aware Minimization Optimizer 稀疏摄动锐度感知最小化优化器的系统研究

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-06-27 DOI: 10.1109/tpami.2025.3581310

Peng Mi, Li Shen, Tianhe Ren, Yiyi Zhou, Tianshuo Xu, Xiaoshuai Sun, Tongliang Liu, Rongrong Ji, Dacheng Tao

引用次数: 0

V3D: Video Diffusion Models are Effective 3D Generators V3D：视频扩散模型是有效的3D生成器

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-06-27 DOI: 10.1109/tpami.2025.3581312

Zilong Chen, Yikai Wang, Feng Wang, Zhengyi Wang, Fuchun Sun, Huaping Liu

引用次数: 0

Revisiting Essential and Nonessential Settings of Evidential Deep Learning 重新审视证据深度学习的必要和非必要设置

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-06-26 DOI: 10.1109/tpami.2025.3583410

Mengyuan Chen, Junyu Gao, Changsheng Xu

引用次数: 0

CAT+: Investigating and Enhancing Audio-visual Understanding in Large Language Models. CAT+：研究和增强大型语言模型中的视听理解。

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-06-25 DOI: 10.1109/tpami.2025.3582389

Qilang Ye,Zitong Yu,Rui Shao,Yawen Cui,Xiangui Kang,Xin Liu,Philip Torr,Xiaochun Cao

{"title":"CAT+: Investigating and Enhancing Audio-visual Understanding in Large Language Models.","authors":"Qilang Ye,Zitong Yu,Rui Shao,Yawen Cui,Xiangui Kang,Xin Liu,Philip Torr,Xiaochun Cao","doi":"10.1109/tpami.2025.3582389","DOIUrl":"https://doi.org/10.1109/tpami.2025.3582389","url":null,"abstract":"Multimodal Large Language Models (MLLMs) have gained significant attention due to their rich internal implicit knowledge for cross-modal learning. Although advances in bringing audio-visuals into LLMs have resulted in boosts for a variety of Audio-Visual Question Answering (AVQA) tasks, they still face two crucial challenges: 1) audio-visual ambiguity, and 2) audio-visual hallucination. Existing MLLMs can respond to audio-visual content, yet sometimes fail to describe specific objects due to the ambiguity or hallucination of responses. To overcome the two aforementioned issues, we introduce the CAT+, which enhances MLLM to ensure more robust multimodal understanding. We first propose the Sequential Question-guided Module (SQM), which combines tiny transformer layers and cascades Q-Formers to realize a solid audio-visual grounding. After feature alignment and high-quality instruction tuning, we introduce Ambiguity Scoring Direct Preference Optimization (AS-DPO) to correct the problem of CAT+ bias toward ambiguous descriptions. To explore the hallucinatory deficits of MLLMs in dynamic audio-visual scenes, we build a new Audio-visual Hallucination Benchmark, named AVHbench. This benchmark detects the extent of MLLM's hallucinations across three different protocols in the perceptual object, counting, and holistic description tasks. Extensive experiments across video-based understanding, open-ended, and close-ended AVQA demonstrate the superior performance of our method.","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"26 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144488043","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

Accelerated Self-Supervised Multi-Illumination Color Constancy with Hybrid Knowledge Distillation. 基于混合知识蒸馏的加速自监督多光照颜色恒常性。

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-06-25 DOI: 10.1109/tpami.2025.3583090

Ziyu Feng,Bing Li,Congyan Lang,Zheming Xu,Haina Qin,Juan Wang,Weihua Xiong

{"title":"Accelerated Self-Supervised Multi-Illumination Color Constancy with Hybrid Knowledge Distillation.","authors":"Ziyu Feng,Bing Li,Congyan Lang,Zheming Xu,Haina Qin,Juan Wang,Weihua Xiong","doi":"10.1109/tpami.2025.3583090","DOIUrl":"https://doi.org/10.1109/tpami.2025.3583090","url":null,"abstract":"Color constancy, the human visual system's ability to perceive consistent colors under varying illumination conditions, is crucial for accurate color perception. Recently, deep learning algorithms have been introduced into this task and have achieved remarkable achievements. However, existing methods are limited by the scale of current multi-illumination datasets and model size, hindering their ability to learn discriminative features effectively and their practical value for deployment in cameras. To overcome these limitations, this paper proposes a multi-illumination color constancy approach based on self-supervised learning and knowledge distillation. This approach includes three phases: self-supervised pre-training, supervised fine-tuning, and knowledge distillation. During the pre-training phase, we train Transformer-based and U-Net based encoders by two pretext tasks: light normalization task to learn lighting color contextual representation and grayscale colorization task to acquire objects' inherent color information. For the downstream color constancy task, we fine-tune the encoders and design a lightweight decoder to obtain better illumination distributions with fewer parameters. During the knowledge distillation phase, we introduce a hybrid knowledge distillation technique to align CNN features with those of Transformer and U-Net respectively. Our proposed method outperforms state-of-the-art techniques on multi-illumination and single-illumination benchmarks. Extensive ablation studies and visualizations confirm the effectiveness of our model.","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"63 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144488045","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

On the Trade-off between Flatness and Optimization in Distributed Learning 分布式学习中平坦性与优化的权衡

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-06-25 DOI: 10.1109/tpami.2025.3583104

Ying Cao, Zhaoxian Wu, Kun Yuan, Ali H. Sayed

引用次数: 0

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation. 基于场景级标注的点云分割高质量伪标记。

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-06-25 DOI: 10.1109/tpami.2025.3583071

Lunhao Duan,Shanshan Zhao,Xingxing Weng,Jing Zhang,Gui-Song Xia

{"title":"High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation.","authors":"Lunhao Duan,Shanshan Zhao,Xingxing Weng,Jing Zhang,Gui-Song Xia","doi":"10.1109/tpami.2025.3583071","DOIUrl":"https://doi.org/10.1109/tpami.2025.3583071","url":null,"abstract":"This paper investigates indoor point cloud semantic segmentation under scene-level annotation, which is less explored compared to methods relying on sparse point-level labels. In the absence of precise point-level labels, current methods first generate point-level pseudo-labels, which are then used to train segmentation models. However, generating accurate pseudo-labels for each point solely based on scene-level annotations poses a considerable challenge, substantially affecting segmentation performance. Consequently, to enhance accuracy, this paper proposes a high-quality pseudo-label generation framework by exploring contemporary multi-modal information and region-point semantic consistency. Specifically, with a cross-modal feature guidance module, our method utilizes 2D-3D correspondences to align point cloud features with corresponding 2D image pixels, thereby assisting point cloud feature learning. To further alleviate the challenge presented by the scene-level annotation, we introduce a region-point semantic consistency module. It produces regional semantics through a region-voting strategy derived from point-level semantics, which are subsequently employed to guide the point-level semantic predictions. Leveraging the aforementioned modules, our method can rectify inaccurate point-level semantic predictions during training and obtain high-quality pseudo-labels. Significant improvements over previous works on ScanNet v2 and S3DIS datasets under scene-level annotation can demonstrate the effectiveness. Additionally, comprehensive ablation studies validate the contributions of our approach's individual components. The code is available at https://github.com/LHDuan/WSegPC.","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"148 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144488047","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

h-calibration: Rethinking Classifier Recalibration with Probabilistic Error-Bounded Objective h-校准：基于概率误差有界目标的分类器再校准

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-06-24 DOI: 10.1109/tpami.2025.3582796

Wenjian Huang, Guiping Cao, Jiahao Xia, Jingkun Chen, Hao Wang, Jianguo Zhang

引用次数: 0

CLEAN: Category Knowledge-Driven Compression Framework for Efficient 3D Object Detection. CLEAN：分类知识驱动的压缩框架，用于高效的3D目标检测。

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-06-24 DOI: 10.1109/tpami.2025.3582706

Haonan Zhang,Longjun Liu,Fei Hui,Bo Zhang,Hengmin Zhang,Zhiyuan Zha

{"title":"CLEAN: Category Knowledge-Driven Compression Framework for Efficient 3D Object Detection.","authors":"Haonan Zhang,Longjun Liu,Fei Hui,Bo Zhang,Hengmin Zhang,Zhiyuan Zha","doi":"10.1109/tpami.2025.3582706","DOIUrl":"https://doi.org/10.1109/tpami.2025.3582706","url":null,"abstract":"Deep neural networks (DNNs) are potent in LiDAR-based 3D object detection (LiDAR-3DOD), yet their deployment remains daunting due to their cumbersome parameters and computations. Knowledge distillation (KD) is promising for compressing DNNs in LiDAR-3DOD. However, most existing KD methods transfer inadequate knowledge between homogeneous detectors, and do not thoroughly explore optimal student architectures, resulting in insufficient gains for compact student detectors. To this end, we propose a category knowledge-driven compression framework to achieve efficient LiDAR-based 3D detectors. Firstly, we distill knowledge from two-stage teacher detectors to one-stage student detectors, overcoming the limitations of homogeneous pairs. To conduct KD in these heterogeneous pairs, we explore the gap between heterogeneous detectors, and introduce category knowledge-driven KD (CaKD), which includes both student-oriented distillation and two-stage-oriented label assignment distillation. Secondly, to search for the optimal architecture of compact student detectors, we introduce a masked category knowledge-driven structured pruning scheme. This scheme evaluates filter importance by analyzing the changes in category predictions related to foreground regions before and after filter removal, and prunes the less important filters accordingly. Finally, we propose a modified IoU-aware redundancy elimination module to remove redundant false positive samples, thereby further improving the accuracy of detectors. Experiments on various point cloud datasets demonstrate that our method delivers impressive results. For example, on KITTI, several compressed one-stage detectors outperform two-stage detectors in both efficiency and accuracy. Besides, on WOD-mini, our framework reduces the memory footprint of CenterPoint by 5.2× and improves the L2 mAPH by 0.55$%$.","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"54 90 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144478732","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

Hybrid Gaussian Deformation for Efficient Remote Sensing Object Detection. 基于混合高斯变形的高效遥感目标检测。

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-06-24 DOI: 10.1109/tpami.2025.3583006

Wenda Zhao,Xiao Zhang,Haipeng Wang,Huchuan Lu

{"title":"Hybrid Gaussian Deformation for Efficient Remote Sensing Object Detection.","authors":"Wenda Zhao,Xiao Zhang,Haipeng Wang,Huchuan Lu","doi":"10.1109/tpami.2025.3583006","DOIUrl":"https://doi.org/10.1109/tpami.2025.3583006","url":null,"abstract":"Large-scale high-resolution remote sensing images (LSHR) are increasingly adopted for object detection, since they capture finer details. However, LSHR imposes a substantial computational cost. Existing methods explore lightweight backbones and advanced oriented bounding box regression mechanisms. Nevertheless, they still rely on high-resolution inputs to maintain detection accuracy. We observe that LSHR comprise extensive background areas that can be compressed to reduce unnecessary computation, while object regions contain details that can be reserved to improve detection accuracy. Thus, we propose a hybrid Gaussian deformation module that dynamically adjusts the sampling density at each location based on its relevance to the detection task, i.e., high-density sampling preserves more object regions and better retains detailed features, while low-density sampling diminishes the background proportion. Further, we introduce a bilateral deform-uniform detection framework to exploit the potential of the deformed sampled low-resolution images and original high-resolution images. Specifically, a deformed deep backbone takes the deformed sampled images as inputs to produce high-level semantic information, and a uniform shallow backbone takes the original high-resolution images as inputs to generate precise spatial location information. Moreover, we incorporate a deformation-aware feature registration module that calibrates the spatial information of deformed features, preventing regression degenerate solutions while maintaining feature activation. Subsequently, we introduce a feature relationship interaction fusion module to balance the contributions of features from both deformed and uniform backbones. Comprehensive experiments on three challenging datasets show that our method achieves superior performance compared with the state-of-the-art methods.","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"20 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144478789","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