Modeling Noisy Annotations for Point-Wise Supervision
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[1] Shaopeng Yang,et al. CrowdFormer: An Overlap Patching Vision Transformer for Top-Down Crowd Counting , 2022, IJCAI.
[2] Antoni B. Chan,et al. Crowd Counting in the Frequency Domain , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[3] Kevin J Liang,et al. Few-shot Learning with Noisy Labels , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[4] Fang Wen,et al. Large-Scale Pre-training for Person Re-identification with Noisy Labels , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[5] Yanwei Fu,et al. Scalable Penalized Regression for Noise Detection in Learning with Noisy Labels , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[6] Yaowei Wang,et al. Boosting Crowd Counting via Multifaceted Attention , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[7] Dingkang Liang,et al. An End-to-End Transformer Model for Crowd Localization , 2022, ECCV.
[8] Antoni B. Chan,et al. Dynamic Momentum Adaptation for Zero-Shot Cross-Domain Crowd Counting , 2021, ACM Multimedia.
[9] Yiqiu Shen,et al. Adaptive Early-Learning Correction for Segmentation from Noisy Annotations , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[10] Shenghua Gao,et al. Crowd Counting With Partial Annotations in an Image , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[11] Yalin Zheng,et al. Spatial Uncertainty-Aware Semi-Supervised Crowd Counting , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[12] Ying Tai,et al. Rethinking Counting and Localization in Crowds: A Purely Point-Based Framework , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[13] Erkun Yang,et al. Understanding and Improving Early Stopping for Learning with Noisy Labels , 2021, NeurIPS.
[14] Antoni B. Chan,et al. Progressive Unsupervised Learning for Visual Object Tracking , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[15] Bin Xiao,et al. Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Pascal Fua,et al. Leveraging Self-Supervision for Cross-Domain Crowd Counting , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Se-Young Yun,et al. FINE Samples for Learning with Noisy Labels , 2021, NeurIPS.
[18] D. Samaras,et al. Distribution Matching for Crowd Counting , 2020, NeurIPS.
[19] Antoni B. Chan,et al. Kernel-Based Density Map Generation for Dense Object Counting , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[20] Wei Wu,et al. Adaptive Dilated Network With Self-Correction Supervision for Counting , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[21] Vishal M. Patel,et al. JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[22] Luc Van Gool,et al. Probabilistic Regression for Visual Tracking , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[23] Qi Wang,et al. NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[24] Gang Yu,et al. SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines , 2019, AAAI.
[25] Antoni B. Chan,et al. Adaptive Density Map Generation for Crowd Counting , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[26] Vishal M. Patel,et al. Multi-Level Bottom-Top and Top-Bottom Feature Fusion for Crowd Counting , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[27] Thomas S. Huang,et al. HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[28] Guanbin Li,et al. Crowd Counting With Deep Structured Scale Integration Network , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[29] James Bailey,et al. Symmetric Cross Entropy for Robust Learning With Noisy Labels , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[30] Yihong Gong,et al. Bayesian Loss for Crowd Count Estimation With Point Supervision , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[31] Fahad Shahbaz Khan,et al. Learning the Model Update for Siamese Trackers , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[32] R. Venkatesh Babu,et al. Locate, Size, and Count: Accurately Resolving People in Dense Crowds via Detection , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[33] Baoyuan Wu,et al. Residual Regression With Semantic Prior for Crowd Counting , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[34] Thomas Brox,et al. Robust Learning Under Label Noise With Iterative Noise-Filtering , 2019, ArXiv.
[35] L. Gool,et al. Learning Discriminative Model Prediction for Tracking , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[36] Wei Lin,et al. Learning From Synthetic Data for Crowd Counting in the Wild , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[37] Dong Liu,et al. Deep High-Resolution Representation Learning for Human Pose Estimation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[38] Zhipeng Zhang,et al. Deeper and Wider Siamese Networks for Real-Time Visual Tracking , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[39] Yaser Sheikh,et al. OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[40] Qiang Wang,et al. Fast Online Object Tracking and Segmentation: A Unifying Approach , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[41] Hao Zhu,et al. CrowdPose: Efficient Crowded Scenes Pose Estimation and a New Benchmark , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[42] Michael Felsberg,et al. ATOM: Accurate Tracking by Overlap Maximization , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[43] Yusuke Uchida,et al. Improving Multi-Person Pose Estimation using Label Correction , 2018, ArXiv.
[44] Yan Yan,et al. DSNet: Deep and Shallow Feature Learning for Efficient Visual Tracking , 2018, ACCV.
[45] Kaiqi Huang,et al. GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[46] Yi Liu,et al. Robust Correlation Filter Tracking with Shepherded Instance-Aware Proposals , 2018, ACM Multimedia.
[47] Fan Yang,et al. LaSOT: A High-Quality Benchmark for Large-Scale Single Object Tracking , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[48] Fei Su,et al. Scale Aggregation Network for Accurate and Efficient Crowd Counting , 2018, ECCV.
[49] Haroon Idrees,et al. Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds , 2018, ECCV.
[50] Bingbing Ni,et al. Crowd Counting via Adversarial Cross-Scale Consistency Pursuit , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[51] Wei Wu,et al. High Performance Visual Tracking with Siamese Region Proposal Network , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[52] Antoni B. Chan,et al. Crowd Counting by Adaptively Fusing Predictions from an Image Pyramid , 2018, BMVC.
[53] Yichen Wei,et al. Simple Baselines for Human Pose Estimation and Tracking , 2018, ECCV.
[54] Bernard Ghanem,et al. TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild , 2018, ECCV.
[55] Antoni B. Chan,et al. Learning Dynamic Memory Networks for Object Tracking , 2018, ECCV.
[56] Yuhong Li,et al. CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[57] Song Wang,et al. Learning Dynamic Siamese Network for Visual Object Tracking , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[58] Vishal M. Patel,et al. Generating High-Quality Crowd Density Maps Using Contextual Pyramid CNNs , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[59] Vishal M. Patel,et al. CNN-Based cascaded multi-task learning of high-level prior and density estimation for crowd counting , 2017, 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).
[60] Dit-Yan Yeung,et al. Spatiotemporal Modeling for Crowd Counting in Videos , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[61] Shiv Surya,et al. Switching Convolutional Neural Network for Crowd Counting , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[62] Luca Bertinetto,et al. End-to-End Representation Learning for Correlation Filter Based Tracking , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[63] Ross B. Girshick,et al. Mask R-CNN , 2017, 1703.06870.
[64] Alex Kendall,et al. What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? , 2017, NIPS.
[65] Cewu Lu,et al. RMPE: Regional Multi-person Pose Estimation , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[66] Michael Felsberg,et al. ECO: Efficient Convolution Operators for Tracking , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[67] Michael Felsberg,et al. Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking , 2016, ECCV.
[68] Luca Bertinetto,et al. Fully-Convolutional Siamese Networks for Object Tracking , 2016, ECCV Workshops.
[69] Shenghua Gao,et al. Single-Image Crowd Counting via Multi-Column Convolutional Neural Network , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[70] A. Smeulders,et al. Siamese Instance Search for Tracking , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[71] Jia Deng,et al. Stacked Hourglass Networks for Human Pose Estimation , 2016, ECCV.
[72] Michael Felsberg,et al. Convolutional Features for Correlation Filter Based Visual Tracking , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).
[73] Peter V. Gehler,et al. DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[74] Ming-Hsuan Yang,et al. Object Tracking Benchmark , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[75] Xiaogang Wang,et al. Learning from massive noisy labeled data for image classification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[76] Xiaogang Wang,et al. Cross-scene crowd counting via deep convolutional neural networks , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[77] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[78] Bernt Schiele,et al. 2D Human Pose Estimation: New Benchmark and State of the Art Analysis , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[79] Nagarajan Natarajan,et al. Learning with Noisy Labels , 2013, NIPS.
[80] Shaogang Gong,et al. From Semi-supervised to Transfer Counting of Crowds , 2013, 2013 IEEE International Conference on Computer Vision.
[81] Haroon Idrees,et al. Multi-source Multi-scale Counting in Extremely Dense Crowd Images , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[82] Nuno Vasconcelos,et al. Bayesian Poisson regression for crowd counting , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[83] R. Collins,et al. Marked point processes for crowd counting , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[84] Tieniu Tan,et al. Estimating the number of people in crowded scenes by MID based foreground segmentation and head-shoulder detection , 2008, 2008 19th International Conference on Pattern Recognition.
[85] Nuno Vasconcelos,et al. Privacy preserving crowd monitoring: Counting people without people models or tracking , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[86] Nasser M. Nasrabadi,et al. Pattern Recognition and Machine Learning , 2006, Technometrics.
[87] Antoni B. Chan,et al. Calibration-Free Multi-view Crowd Counting , 2022, ECCV.
[88] Antoni B. Chan,et al. Supplemental for A Generalized Loss Function for Crowd Counting and Localization , 2021 .
[89] Antoni B. Chan,et al. Modeling Noisy Annotations for Crowd Counting , 2020, NeurIPS.
[90] Fanman Meng,et al. Learning with Noisy Class Labels for Instance Segmentation , 2020, ECCV.
[91] Ralph B. D'Agostino,et al. Tests for Departure from Normality , 1973 .