Existing enhancement methods are empirically expected to help the high-level end computer vision task: however, that is observed to not always be the case in practice. We focus on object or face detection in poor visibility enhancements caused by bad weathers (haze, rain) and low light conditions. To provide a more thorough examination and fair comparison, we introduce three benchmark sets collected in real-world hazy, rainy, and lowlight conditions, respectively, with annotated objects/faces. We launched the UG 2+ challenge Track 2 competition in IEEE CVPR 2019, aiming to evoke a comprehensive discussion and exploration about whether and how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios. To our best knowledge, this is the first and currently largest effort of its kind. Baseline results by cascading existing enhancement and detection models are reported, indicating the highly challenging nature of our new data as well as the large room for further technical innovations. Thanks to a large participation from the research community, we are able to analyze representative team solutions, striving to better identify the strengths and limitations of existing mindsets as well as the future directions.Index Terms-Poor visibility environment, object detection, face detection, haze, rain, low-light conditions *The first two authors Wenhan Yang and Ye Yuan contributed equally. Ye Yuan and Wenhan Yang helped prepare the dataset proposed for the UG2+ Challenges, and were the main responsible members for UG2+ Challenge 2019 (Track 2) platform setup and technical support. Wenqi Ren, Jiaying Liu, Walter J. Scheirer, and Zhangyang Wang were the main organizers of the challenge and helped prepare the dataset, raise sponsors, set up evaluation environment, and improve the technical submission. Other authors are the group members of winner teams in UG2+ challenge Track 2 contributing to the winning methods.

This retrospective study aims to examine the association of plasma Epstein-Barr virus- (EBV-) DNA levels with the tumor volume and prognosis in patients with locally advanced nasopharyngeal carcinoma (NPC). A total of 165 patients with newly diagnosed locally advanced NPC were identified from September 2011 to July 2012. EBV-DNA was detected using fluorescence quantitative polymerase chain reaction (PCR) amplification. The tumor volume was calculated by the systematic summation method of computer software. The median copy number of plasma EBV-DNA before treatment was 3790 copies/mL. The median gross tumor volume of the primary nasopharyngeal tumor (GTVnx), the lymph node lesions (GTVnd), and the total GTV before treatment were 72.46, 23.26, and 106.25 cm3, respectively; the EBV-DNA levels were significantly correlated with the GTVnd and the total GTV (P < 0.01). The 2-year overall survival (OS) rates in patients with positive and negative pretreatment plasma EBV-DNA were 100% and 98.4% (P = 1.000), and the disease-free survival (DFS) rates were 94.4% and 80.8% (P = 0.044), respectively. These results indicate that high pretreatment plasma EBV-DNA levels in patients with locally advanced NPC are associated with the degree of lymph node metastasis, tumor burden, and poor prognosis.

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