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Mine Your Own Anatomy: Revisiting Medical Image Segmentation With Extremely Limited Labels IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-09-13 Chenyu You, Weicheng Dai, Fenglin Liu, Yifei Min, Nicha C. Dvornek, Xiaoxiao Li, David A. Clifton, Lawrence Staib, James S. Duncan
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Tuning Vision-Language Models with Multiple Prototypes Clustering IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-09-13 Meng-Hao Guo, Yi Zhang, Tai-Jiang Mu, Sharon X. Huang, Shi-Min Hu
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Weakly-supervised Depth Estimation and Image Deblurring via Dual-Pixel Sensors IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-09-12 Liyuan Pan, Richard Hartley, Liu Liu, Zhiwei Xu, Shah Chowdhury, Yan Yang, Hongguang Zhang, Hongdong Li, Miaomiao Liu
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Label Deconvolution for Node Representation Learning on Large-scale Attributed Graphs against Learning Bias IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-09-12 Zhihao Shi, Jie Wang, Fanghua Lu, Hanzhu Chen, Defu Lian, Zheng Wang, Jieping Ye, Feng Wu
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ES-GNN: Generalizing Graph Neural Networks Beyond Homophily With Edge Splitting IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-09-12 Jingwei Guo, Kaizhu Huang, Rui Zhang, Xinping Yi
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Low-Dimensional Gradient Helps Out-of-Distribution Detection IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-09-12 Yingwen Wu, Tao Li, Xinwen Cheng, Jie Yang, Xiaolin Huang
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Learning From Human Attention for Attribute-Assisted Visual Recognition IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-09-11 Xiao Bai, Pengcheng Zhang, Xiaohan Yu, Jin Zheng, Edwin R. Hancock, Jun Zhou, Lin Gu
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Deep Single Image Defocus Deblurring via Gaussian Kernel Mixture Learning IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-09-10 Yuhui Quan, Zicong Wu, Ruotao Xu, Hui Ji
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HiSC4D: Human-Centered Interaction and 4D Scene Capture in Large-Scale Space Using Wearable IMUs and LiDAR IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-09-10 Yudi Dai, Zhiyong Wang, Xiping Lin, Chenglu Wen, Lan Xu, Siqi Shen, Yuexin Ma, Cheng Wang
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Refining 3D Human Texture Estimation from a Single Image IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-09-10 Said Fahri Altindis, Adil Meric, Yusuf Dalva, Uğur Güdükbay, Aysegul Dundar
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One-Stage Anchor-Free Online Multiple Target Tracking with Deformable Local Attention and Task-Aware Prediction IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-09-10 Weiming Hu, Shaoru Wang, Zongwei Zhou, Jin Gao, Yangxi Li, Stephen Maybank
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A Review of Safe Reinforcement Learning: Methods, Theories and Applications IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-09-10 Shangding Gu, Long Yang, Yali Du, Guang Chen, Florian Walter, Jun Wang, Alois Knoll
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Revealing the Dark Side of Non-Local Attention in Single Image Super-Resolution IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-09-10 Jian-Nan Su, Guodong Fan, Min Gan, Guang-Yong Chen, Wenzhong Guo, C. L. Philip Chen
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R $^{3}$ LIVE++: A Robust, Real-time, Radiance Reconstruction Package with a Tightly-coupled LiDAR-Inertial-Visual State Estimator IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-09-09 Jiarong Lin, Fu Zhang
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Cap4Video++: Enhancing Video Understanding with Auxiliary Captions IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-09-09 Wenhao Wu, Xiaohan Wang, Haipeng Luo, Jingdong Wang, Yi Yang, Wanli Ouyang
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Vision-Centric BEV Perception: A Survey IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-09-09 Yuexin Ma, Tai Wang, Xuyang Bai, Huitong Yang, Yuenan Hou, Yaming Wang, Yu Qiao, Ruigang Yang, Xinge Zhu
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MINN: Learning the Dynamics of Differential-algebraic Equations and Application to Battery Modeling IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-09-09 Yicun Huang, Changfu Zou, Yang Li, Torsten Wik
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Diversifying Policies with Non-Markov Dispersion to Expand the Solution Space IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-09-06 Bohao Qu, Xiaofeng Cao, Yi Chang, Ivor W.Tsang, Yew-Soon Ong
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Variational Label Enhancement for Instance-Dependent Partial Label Learning IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-09-06 Ning Xu, Congyu Qiao, Yuchen Zhao, Xin Geng, Min-Ling Zhang
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Integrating Neural Radiance Fields End-to-End for Cognitive Visuomotor Navigation IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-09-06 Qiming Liu, Haoran Xin, Zhe Liu, Hesheng Wang
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TagCLIP: Improving Discrimination Ability of Zero-Shot Semantic Segmentation IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-09-04 Jingyao Li, Pengguang Chen, Shengju Qian, Shu Liu, Jiaya Jia
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Panoptic-PartFormer++: A Unified and Decoupled View for Panoptic Part Segmentation IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-09-03 Xiangtai Li, Shilin Xu, Yibo Yang, Haobo Yuan, Guangliang Cheng, Yunhai Tong, Zhouchen Lin, Ming-Hsuan Yang, Dacheng Tao
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Efficient Neural Collaborative Search for Pickup and Delivery Problems IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-09-03 Detian Kong, Yining Ma, Zhiguang Cao, Tianshu Yu, Jianhua Xiao
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Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-08-30 Zdravko Marinov, Paul F. Jäger, Jan Egger, Jens Kleesiek, Rainer Stiefelhagen
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Unsupervised Active Visual Search with Monte Carlo Planning under Uncertain Detections IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-08-29 Francesco Taioli, Francesco Giuliari, Yiming Wang, Riccardo Berra, Alberto Castellini, Alessio Del Bue, Alessandro Farinelli, Marco Cristani, Francesco Setti
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Uni-to-Multi Modal Knowledge Distillation for Bidirectional LiDAR-Camera Semantic Segmentation IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-08-29 Tianfang Sun, Zhizhong Zhang, Xin Tan, Yong Peng, Yanyun Qu, Yuan Xie
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Topo-Geometric Analysis of Variability in Point Clouds using Persistence Landscapes IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-08-28 James Matuk, Sebastian Kurtek, Karthik Bharath
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Playing for 3D Human Recovery IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-08-27 Zhongang Cai, Mingyuan Zhang, Jiawei Ren, Chen Wei, Daxuan Ren, Zhengyu Lin, Haiyu Zhao, Lei Yang, Chen Change Loy, Ziwei Liu
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Fast-Vid2Vid++: Spatial-Temporal Distillation for Real-Time Video-to-Video Synthesis IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-08-27 Long Zhuo, Guangcong Wang, Shikai Li, Wayne Wu, Ziwei Liu
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DeepTensor: Low-Rank Tensor Decomposition with Deep Network Priors IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-08-27 Vishwanath Saragadam, Randall Balestriero, Ashok Veeraraghavan, Richard G. Baraniuk
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Frequency-aware Feature Fusion for Dense Image Prediction IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-08-26 Linwei Chen, Ying Fu, Lin Gu, Chenggang Yan, Tatsuya Harada, Gao Huang
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Gaseous Object Detection IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-08-26 Kailai Zhou, Yibo Wang, Tao Lv, Qiu Shen, Xun Cao
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Say No to Freeloader: Protecting Intellectual Property of Your Deep Model IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-08-26 Lianyu Wang, Meng Wang, Huazhu Fu, Daoqaing Zhang
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LTM-NeRF: Embedding 3D Local Tone Mapping in HDR Neural Radiance Field IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-08-23 Xin Huang, Qi Zhang, Ying Feng, Hongdong Li, Qing Wang
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LIA: Latent Image Animator IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-08-23 Yaohui Wang, Di Yang, Francois Bremond, Antitza Dantcheva
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Correlation-Embedded Transformer Tracking: A Single-Branch Framework IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-08-22 Fei Xie, Wankou Yang, Chunyu Wang, Lei Chu, Yue Cao, Chao Ma, Wenjun Zeng
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Multimodal Cross-lingual Summarization for Videos: A Revisit in Knowledge Distillation Induced Triple-stage Training Method IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-08-22 Nayu Liu, Kaiwen Wei, Yong Yang, Jianhua Tao, Xian Sun, Fanglong Yao, Hongfeng Yu, Li Jin, Zhao Lv, Cunhang Fan
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Q-BENCH: A Benchmark for Multi-modal Foundation Models on Low-level Vision from Single Images to Pairs IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-08-21 Zicheng Zhang, Haoning Wu, Erli Zhang, Guangtao Zhai, Weisi Lin
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A Survey on Continual Semantic Segmentation: Theory, Challenge, Method and Application IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-08-21 Bo Yuan, Danpei Zhao
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A Novel and Effective Method to Directly Solve Spectral Clustering IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-08-21 Feiping Nie, Chaodie Liu, Rong Wang, Xuelong Li
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U-Match: Exploring Hierarchy-aware Local Context for Two-view Correspondence Learning IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-08-21 Zizhuo Li, Shihua Zhang, Jiayi Ma
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A Survey on Deep Neural Network Pruning: Taxonomy, Comparison, Analysis, and Recommendations IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-08-21 Hongrong Cheng, Miao Zhang, Javen Qinfeng Shi
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Medical Image Segmentation Review: The Success of U-Net IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-08-21 Reza Azad, Ehsan Khodapanah Aghdam, Amelie Rauland, Yiwei Jia, Atlas Haddadi Avval, Afshin Bozorgpour, Sanaz Karimijafarbigloo, Joseph Paul Cohen, Ehsan Adeli, Dorit Merhof
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CO-Net++: A Cohesive Network for Multiple Point Cloud Tasks at Once with Two-Stage Feature Rectification IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-08-21 Tao Xie, Kun Dai, Qihao Sun, Zhiqiang Jiang, Chuqing Cao, Lijun Zhao, Ke Wang, Ruifeng Li
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Tensorized and Compressed Multi-view Subspace Clustering via Structured Constraint IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-08-20 Wei Chang, Huimin Chen, Feiping Nie, Rong Wang, Xuelong Li
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Unsupervised Part Discovery via Dual Representation Alignment IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-08-19 Jiahao Xia, Wenjian Huang, Min Xu, Jianguo Zhang, Haimin Zhang, Ziyu Sheng, Dong Xu
Object parts serve as crucial intermediate representations in various downstream tasks, but part-level representation learning still has not received as much attention as other vision tasks. Previous research has established that Vision Transformer can learn instance-level attention without labels, extracting high-quality instance-level representations for boosting downstream tasks. In this paper,
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Approaching the Global Nash Equilibrium of Non-convex Multi-player Games IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-08-19 Guanpu Chen, Gehui Xu, Fengxiang He, Yiguang Hong, Leszek Rutkowski, Dacheng Tao
Many machine learning problems can be formulated as non-convex multi-player games. Due to non-convexity, it is challenging to obtain the existence condition of the global Nash equilibrium (NE) and design theoretically guaranteed algorithms. This paper studies a class of non-convex multi-player games, where players' payoff functions consist of canonical functions and quadratic operators. We leverage
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A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-08-19 Chaoqi Chen, Yushuang Wu, Qiyuan Dai, Hong-Yu Zhou, Mutian Xu, Sibei Yang, Xiaoguang Han, Yizhou Yu
Graph Neural Networks (GNNs) have gained momentum in graph representation learning and boosted the state of the art in a variety of areas, such as data mining (e.g., social network analysis and recommender systems), computer vision (e.g., object detection and point cloud learning), and natural language processing (e.g., relation extraction and sequence learning), to name a few. With the emergence of
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Efficient and Robust Point Cloud Registration via Heuristics-Guided Parameter Search IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-04-11 Tianyu Huang, Haoang Li, Liangzu Peng, Yinlong Liu, Yun-Hui Liu
Estimating the rigid transformation with 6 degrees of freedom based on a putative 3D correspondence set is a crucial procedure in point cloud registration. Existing correspondence identification methods usually lead to large outlier ratios (>95% is common), underscoring the significance of robust registration methods. Many researchers turn to parameter search-based strategies (e.g., Branch-and-Bround)
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XGrad: Boosting Gradient-Based Optimizers With Weight Prediction IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-04-11 Lei Guan, Dongsheng Li, Yanqi Shi, Jian Meng
In this paper, we propose a general deep learning training framework XGrad which introduces weight prediction into the popular gradient-based optimizers to boost their convergence and generalization when training the deep neural network (DNN) models. In particular, ahead of each mini-batch training, the future weights are predicted according to the update rule of the used optimizer and are then applied
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On the Consistency and Large-Scale Extension of Multiple Kernel Clustering IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-04-11 Weixuan Liang, Chang Tang, Xinwang Liu, Yong Liu, Jiyuan Liu, En Zhu, Kunlun He
Existing multiple kernel clustering (MKC) algorithms have two ubiquitous problems. From the theoretical perspective, most MKC algorithms lack sufficient theoretical analysis, especially the consistency of learned parameters, such as the kernel weights. From the practical perspective, the high complexity makes MKC unable to handle large-scale datasets. This paper tries to address the above two issues
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Prototype-Based Semantic Segmentation IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-04-10 Tianfei Zhou, Wenguan Wang
Deep learning based semantic segmentation solutions have yielded compelling results over the preceding decade. They encompass diverse network architectures (FCN based or attention based), along with various mask decoding schemes (parametric softmax based or pixel-query based). Despite the divergence, they can be grouped within a unified framework by interpreting the softmax weights or query vectors
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Hybrid Open-Set Segmentation With Synthetic Negative Data IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-04-10 Matej Grcić, Siniša Šegvić
Open-set segmentation can be conceived by complementing closed-set classification with anomaly detection. Many of the existing dense anomaly detectors operate through generative modelling of regular data or by discriminating with respect to negative data. These two approaches optimize different objectives and therefore exhibit different failure modes. Consequently, we propose a novel anomaly score
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Bayesian Optimization for Sparse Neural Networks With Trainable Activation Functions IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-04-10 Mohamed Fakhfakh, Lotfi Chaari
In the literature on deep neural networks, there is considerable interest in developing activation functions that can enhance neural network performance. In recent years, there has been renewed scientific interest in proposing activation functions that can be trained throughout the learning process, as they appear to improve network performance, especially by reducing overfitting. In this paper, we
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Learning Local and Global Temporal Contexts for Video Semantic Segmentation IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-04-10 Guolei Sun, Yun Liu, Henghui Ding, Min Wu, Luc Van Gool
Contextual information plays a core role for video semantic segmentation (VSS). This paper summarizes contexts for VSS in two-fold: local temporal contexts (LTC) which define the contexts from neighboring frames, and global temporal contexts (GTC) which represent the contexts from the whole video. As for LTC, it includes static and motional contexts, corresponding to static and moving content in neighboring
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PPDM++: Parallel Point Detection and Matching for Fast and Accurate HOI Detection IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-04-10 Yue Liao, Si Liu, Yulu Gao, Aixi Zhang, Zhimin Li, Fei Wang, Bo Li
Human-Object Interaction (HOI) detection aims to understand human activities by detecting interaction triplets. Previous HOI detection methods adopt a two-stage instance-driven paradigm. Unfortunately, many non-interactive human-object pairs generated by the first stage are the main obstacle impeding HOI detectors from high efficiency and promising performance. To remedy this, we propose a novel top-down
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STMixer: A One-Stage Sparse Action Detector IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-04-10 Tao Wu, Mengqi Cao, Ziteng Gao, Gangshan Wu, Limin Wang
Traditional video action detectors typically adopt the two-stage pipeline, where a person detector is first employed to generate actor boxes and then 3D RoIAlign is used to extract actor-specific features for action recognition. This detection paradigm requires multi-stage training and inference, and the feature sampling is only constrained inside the box, failing to effectively leverage richer context
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A Modular Neural Motion Retargeting System Decoupling Skeleton and Shape Perception IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-04-10 Jiaxu Zhang, Zhigang Tu, Junwu Weng, Junsong Yuan, Bo Du
Motion mapping between characters with different structures but corresponding to homeomorphic graphs, meanwhile preserving motion semantics and perceiving shape geometries, poses significant challenges in skinned motion retargeting. We propose M-R $^{2}$ ET, a modular neural motion retargeting system to comprehensively address these challenges. The key insight driving M-R $^{2}$ ET is its capacity
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PERF: Panoramic Neural Radiance Field From a Single Panorama IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-04-10 Guangcong Wang, Peng Wang, Zhaoxi Chen, Wenping Wang, Chen Change Loy, Ziwei Liu
Neural Radiance Field (NeRF) has achieved substantial progress in novel view synthesis given multi-view images. Recently, some works have attempted to train a NeRF from a single image with 3D priors. They mainly focus on a limited field of view with a few occlusions, which greatly limits their scalability to real-world 360-degree panoramic scenarios with large-size occlusions. In this paper, we present
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Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects IEEE Trans. Pattern Anal. Mach. Intell. (IF 20.8) Pub Date : 2024-04-10 Kexin Zhang, Qingsong Wen, Chaoli Zhang, Rongyao Cai, Ming Jin, Yong Liu, James Y. Zhang, Yuxuan Liang, Guansong Pang, Dongjin Song, Shirui Pan
Self-supervised learning (SSL) has recently achieved impressive performance on various time series tasks. The most prominent advantage of SSL is that it reduces the dependence on labeled data. Based on the pre-training and fine-tuning strategy, even a small amount of labeled data can achieve high performance. Compared with many published self-supervised surveys on computer vision and natural language