相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。
Article
Computer Science, Information Systems
Mingjie Wang et al.
Summary: This paper proposes a novel network called STNet for accurate crowd counting. STNet consists of two key components: Scale-Tree Diversity Enhancer and Multi-level Auxiliator. By enriching scale diversity and exploiting shared characteristics at multiple levels, STNet can significantly improve the accuracy of crowd counting.
IEEE TRANSACTIONS ON MULTIMEDIA
(2023)
Article
Computer Science, Information Systems
Mahesh Kumar Krishna Reddy et al.
Summary: This paper addresses the problem of image-based crowd counting and proposes a new problem setup called unlabeled scene-adaptive crowd counting. By using unlabeled images from the target scene, the proposed problem setup aims to adapt the crowd counting model to specific scenes. The authors introduce the AdaCrowd framework, which consists of a crowd counting network and a guiding network, and demonstrate its effectiveness through experimental results.
IEEE TRANSACTIONS ON MULTIMEDIA
(2022)
Article
Computer Science, Artificial Intelligence
Vikas Verma et al.
Summary: Interpolation Consistency Training (ICT) is a simple and efficient algorithm for training Deep Neural Networks in the semi-supervised learning paradigm, which moves the decision boundary to low-density regions of the data distribution in classification problems. Experimental results show that ICT achieves state-of-the-art performance when applied to CIFAR-10 and SVHN benchmark datasets.
Article
Engineering, Electrical & Electronic
Shaowei Hou et al.
Summary: Person search is a technique that aims to locate and retrieve specific pedestrians in scene images, and it includes two subtasks: pedestrian detection and person re-identification. We propose a novel Feature Compactness (FC) Loss and a pedestrian attention module (PAM) to improve the accuracy of person search.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Deepak Babu Sam et al.
Summary: This paper proposes a completely self-supervised method for dense crowd counting task without the need for labeled images. By leveraging the power law distribution of natural crowds, it effectively learns crowd features and achieves significant counting performance.
COMPUTER VISION, ECCV 2022, PT XXXI
(2022)
Article
Computer Science, Artificial Intelligence
Qi Wang et al.
Summary: In the past decade, crowd counting and localization have gained much attention from researchers due to their wide range of applications. The NWPU-Crowd dataset is constructed to address the issue of small-scale datasets, containing a large number of annotated heads with points and boxes. A benchmark website is developed for impartial evaluation of different methods, providing researchers with a platform to submit test results and analyze new challenges in the field.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Qingyu Song et al.
Summary: This paper introduces a purely point-based framework for joint crowd counting and individual localization, with a new performance evaluation metric. By directly predicting point proposals with Point to Point Network (P2PNet), which is consistent with human annotations, it achieves significant improvement over existing methods.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Article
Computer Science, Information Systems
Xiaoheng Jiang et al.
Summary: In this study, a novel density-aware convolutional neural network (DensityCNN) method is proposed to perform crowd counting by learning density-level classification and density map estimation. Extensive experiments demonstrate the high effectiveness of the proposed method across multiple datasets.
IEEE TRANSACTIONS ON MULTIMEDIA
(2021)
Article
Computer Science, Information Systems
Cairong Zhao et al.
IEEE TRANSACTIONS ON MULTIMEDIA
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Xialei Liu et al.
2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Zan Shen et al.
2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
(2018)
