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您的检索式：作者名="Ruochen Fan"
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| 1 | S4Net: Single stage salient-instance segmentation显示文摘In this paper, we consider salient instance segmentation. As well as producing bounding boxes,our network also outputs high-quality instance-level segments as initial selections to indicate the regions of interest. Taking into account the category-independent property of each target, we design a single stage salient instance segmentation framework, with a novel segmentation branch. Our new branch regards not only local context inside each detection window but also the surrounding context, enabling us to distinguish instances in the same scope even with partial occlusion.Our network is end-to-end trainable and is fast(running at 40 fps for images with resolution 320 × 320). We evaluate our approach on a publicly available benchmark and show that it outperforms alternative solutions. We also provide a thorough analysis of our design choices to help readers better understand the function of each part of our network. Source code can be found at http://gffzz188fe103f8f1460asxxqvoo6kopcc65p6.ffgz.tsg.suse.edu.cn/Ruochen Fan/S4 Net. | Ruochen Fan Ming-Ming Cheng Qibin Hou Tai-Jiang Mu Jingdong Wang Shi-Min Hu | 2020 | Computational Visual Media2020，6，2： | 2 |
| 2 | Robust tracking-by-detection using a selection and completion mechanism显示文摘It is challenging to track a target continuously in videos with long-term occlusion,or objects which leave then re-enter a scene.Existing tracking algorithms combined with onlinetrained object detectors perform unreliably in complex conditions, and can only provide discontinuous trajectories with jumps in position when the object is occluded. This paper proposes a novel framework of tracking-by-detection using selection and completion to solve the abovementioned problems. It has two components, tracking and trajectory completion. An offline-trained object detector can localize objects in the same category as the object being tracked. The object detector is based on a highly accurate deep learning model. The object selector determines which object should be used to re-initialize a traditional tracker. As the object selector is trained online,it allows the framework to be adaptable. During completion, a predictive non-linear autoregressive neural network completes any discontinuous trajectory.The tracking component is an online real-time algorithm, and the completion part is an after-theevent mechanism. Quantitative experiments show a significant improvement in robustness over prior stateof-the-art methods. | Ruochen Fan Fang-Lue Zhang Min Zhang Ralph R.Martin | 2017 | Computational Visual Media2017，3，3： | 0 |
| 3 | The receptor-like cytoplasmic kinase OsRLCK118 regulates plant development and basal immunity in rice(Oryza sativa L.)显示文摘Receptor-like cytoplasmic kinases(RLCKs),which belong to a large subgroup of receptor-like kinases in plants,play crucial roles in plant development and immunity.However,their functions and regulatory mechanisms in plants remain unclear.Here,we report functional characterization of OsRLCK118 from the OsRLCK34 subgroup in rice(Oryza sativa L.).Expression of OsRLCK118 could be induced by infections with Xanthomonas oryzae pv.oryzae(Xoo)strains PXO68 and PXO99.Silencing of OsRLCK118 altered plant height,flag-leaf angle and second-topleaf angle.Silencing of OsRLCK118 also resulted in increasing susceptibility to Xoo and Magnaporthe oryzae(M.oryzae)in rice plants.OsRLCK118 knock-out plants were more sensitive to bacterial blight whereas OsRLCK118 overexpressor plants exhibited increased disease resistance.Expression levels of pathogenesis-related genes of OsPAL1,OsNH1,OsICS1,OsPR1a,OsPR5 and OsPR10 were reduced in the rlck118 mutant compared to wild-type rice(Dongjin)and knock-out of OsRLCK118 compromised the production of reactive oxygen species.These results suggest that OsRLCK118 may modulate basal resistance to Xoo and M.oryzae,possibly through regulation of ROS burst and hormone mediated defense signaling pathway. | Xiaorong Xiao Rui Wang Wenya Guo Shahneela Khaskhali Ruochen Fan Rui Zhao Chunxia Li Chaozu He Xiaolei Niu Yinhua Chen | 2022 | Tropical Plants2022，1，1： | 0 |
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