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Text-based person search aims to simultaneously localize and identify the target person based on query text from uncropped scene images, which can be regarded as the unified task of person detection and text-based person retrieval task. In this work, we propose a large-scale benchmark dataset named PRW-TPS-CN based on the widely used person search dataset PRW. Our dataset contains 47,102 sentences, which means there is quite more information than existing dataset. These texts precisely describe the person images from top to bottom, which in line with the natural description order. We also provide both Chinese and English descriptions in our dataset for more comprehensive evaluation. These characteristics make our dataset more applicable. To alleviate the inconsistency between person detection and text-based person retrieval, we take advantage of the rich texts in PRW-TPS-CN dataset. We propose to aggregate multiple texts as text prototypes to maintain the prominent text features of a person, which can better reflect the whole character of a person. The overall prototypes lead to generating the image attention map to eliminate the detection misalignment causing the decrease of text-based person retrieval. Thus, the inconsistency between person detection and text-based person retrieval is largely alleviated. We conduct extensive experiments on the PRW-TPS-CN dataset. The experimental results show the PRW-TPS-CN dataset's effectiveness and the state-of-the-art performance of our approach., Comment: 11 pages, 5 figures

Since the beginning of the COVID-19 spreading, the number of studies on the epidemic models increased dramatically. It is important for policymakers to know how the disease will spread and what are the effects of the policies and environment on the spreading. In this paper, we propose two extensions to the standard SIR model: (a) we consider the prevention measures adopted based on the current severity of the infection. Those measures are adaptive and change over time; (b) multiple cities and regions are considered, with population movements between those cities and regions, while taking into account that each region may have different prevention measures. Although the adaptive measures and mobility of the population were often observed during the pandemic, these effects are rarely explicitly modeled and studied in the classical epidemic models. The model we propose gives rise to a plateau phenomenon: the number of people infected by the disease stays at the same level during an extended period of time. We show what are conditions need to be met in order for the spreading to exhibit a plateau period in a single city. In addition, this phenomenon is interdependent when considering multiple cities. We verify from the real-world data that the plateau phenomenon does exist in many regions of the world in the current COVID-19 development. Finally, we provide theoretical analysis on the plateau phenomenon for the single-city model and derive a series of results on the emergence and the ending of the plateau, as well as on the height and length of the plateau. Our theoretical results match well with our experimental findings.