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.
Fruit color is an important economic trait. The color of red walnut cultivars is mainly attributed to anthocyanins. The aim of this study was to explore the differences in the molecular mechanism of leaf and peel color change between red and green walnut. A reference transcriptome of walnut was sequenced and annotated to identify genes related to fruit color at the ripening stage. More than 290 million high-quality reads were assembled into 39,411 genes using a combined assembly strategy. Using Illumina digital gene expression profiling, we identified 4568 differentially expressed genes (DEGs) between red and green walnut leaf and 3038 DEGs between red and green walnut peel at the ripening stage. We also identified some transcription factor families (MYB, bHLH, and WD40) involved in the control of anthocyanin biosynthesis. The trends in the expression levels of several genes encoding anthocyanin biosynthetic enzymes and transcription factors in the leaf and peel of red and green walnut were verified by quantitative real-time PCR. Together, our results identified the genes involved in anthocyanin accumulation in red walnut. These data provide a valuable resource for understanding the coloration of red walnut.
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