视觉与智能学习近期期刊阅读与相关知识学习

近期文章与代码阅读：

[1]. Cairong Zhao, Zhicheng Chen et al.
"Context-Aware Feature Learning for Noise Robust Person Search（用于噪声鲁棒人员搜索的上下文感知特征学习）.
" IEEE Transactions on Circuits and Systems for Video Technology (2022). [pdf][code]

[1]. Cairong Zhao, Liang Zhu et al.
"Detecting Overlapped Objects in X-ray Security Imagery by a Label-aware Mechanism（利用标签感知机制检测X射线安全图像中的重叠物体）.
" IEEE Transactions on Information Forensics and Security (2022). [pdf][code]

[1]. 赵才荣*, 齐鼎, 窦曙光, 涂远鹏, 孙添力, 柏松, 蒋忻洋, 白翔*,苗夺谦*. 智能视频监控关键技术: 行人再识别研究综述, 中国科学.信息科学 2021, 2021, 51（12）:979-2015. [pdf]

[2]. Cairong Zhao, Yuanpeng Tu et al.
Salience-Guided Iterative Asymmetric Mutual Hashing for Fast Person Re-identification（显著性引导的迭代非对称互哈希快速身份识别）
[J]. IEEE Transactions on Image Processing, 2021. [pdf][code]

[3]. Cairong Zhao, Shuyang Feng, Brian Nlong Zhao, Zhijun Ding, Jun Wu, Fuming Shen, and Hengtao Shen.
Scene Text Image Super-Resolution via Parallelly Contextual Attention Network（基于并行上下文注意网络的场景文本图像超分辨率。）.
In Proceedings of the 29th ACM International Conference on Multimedia, 2021, Oral. [pdf][code]

[4]. Shaowei Hou, Cairong Zhao*. 
"Improved Instance Discrimination and Feature Compactness for End-to-End Person Search（改进了端到端人员搜索的实例区分和特征紧凑性）.
" IEEE Transactions on Circuits and Systems for Video Technology, 2021. [pdf][code]

[5]. Cairong Zhao, Xinbi Lv, Shuguang Dou, Shanshan Zhang, Jun Wu, Liang Wang.
Incremental Generative Occlusion Adversarial Suppression Network for Person ReID（用于人的增量生成遮挡对抗抑制网络）.
IEEE Transactions on Image Processing, 2021. [pdf][code]

[6]. Cairong Zhao*, Xinbi Lv*, Zhang Zhang, Wangmeng Zuo, Jun Wu, Duoqian Miao. 
Deep Fusion Feature Representation Learning with Hard Mining Center-Triplet Loss for Person Re-identification（具有硬挖掘中心的深度融合特征表示学习——用于个人再识别的三元组丢失）.
IEEE Transactions on Multimedia, 2020. [pdf][code]

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阅读顺序：

[5]. Cairong Zhao, Xinbi Lv, Shuguang Dou, Shanshan Zhang, Jun Wu, Liang Wang.
Incremental Generative Occlusion Adversarial Suppression Network for Person ReID（用于人的增量生成遮挡对抗抑制网络）.
IEEE Transactions on Image Processing, 2021. [pdf][code]