ID 原文 译文
39966 本文在一个与ImageNet相容的数据集上做了大量实验，实验结果表明本文方法所得的对抗样本在黑盒攻击性能上有显著提升。 Extensive experiments on an ImageNet-compatible dataset show that our proposed method can generate more transferable adversarial examples.
39967 并且，本文最佳攻击组合能以86.2%的平均成功率欺骗6种先进防御模型，相比目前最强攻击方法提升约8.0%。 In addition, our best attack can fool six state-of-the-art defense models with an average success rate of 86.2%, and deliver 8.0% success rate increasement compared with the state-of-the-art gradient-based attack.
39968 车体表面损伤检测是计算机视觉中的经典问题。 Car body surface damage detection is a classic problem in computer vision.
39969 车体表面损伤检测的主要瓶颈在于图像中损伤实例的不同尺度影响了分割的精度与效率。 The main bottleneck of car body surface damage detection lies in the different scales of damage instances in the image, which affects the accuracy and efficiency of segmentation.
39970 本文采用单阶段语义分割网络（YOLACT++）进行车体表面的损伤检测，通过结合EfficientNet设计主干网络提高分割效率，并通过改进损失函数优化YOLACT++中目标实例Mask的生成，实验中用深度学习标注实验数据集进行训练测试。 In this paper, we use a single-stage semantic segmentation network(YOLACT++)for damage detection on the car body surface, combine EfficientNet to design a backbone network to improve segmentation efficiency, and improve the loss function optimization YOLACT++to generate the target instance Mask in the experiment.
39971 实验表明，改进后的YOLACT++降低了Mask生成误差，检测的实时帧率提高到35帧/s，同时也提高了YOLACT++进行实例分割的精度。 Experimental data are marked by deep learning, and results show that the improved YOLACT++detection frame rate is increased to 35 frame/s, which reduces the mask generation error and improves the instance segmentation accuracy of YOLACT++.
39972 随着海洋资源勘探和海洋污染物监控工作的开展，水文数据的监测和采集等已经成为重要的研究方向。 With the development of marine resource exploration and marine pollutant monitoring, monitoring and collection of hydrologic data have become an important research direction.
39973 其中，水下无线传感器网络在水文数据采集过程中起着举足轻重的作用。 Among them, underwater wireless sensor networks play an important role in hydrological data acquisition.
39974 本文研究的是水下无线传感器二维监测网络模型中，传感器节点数据采集的问题， This paper studies the data collection of sensor nodes in the two-dimensional monitoring model network of underwater wireless sensors.
39975 其设计方法是通过自组织映射（Selforganizing mapping，SOM）对传感器节点进行路径最优化处理，结合优化的路径图形和K-means算法找到路径内部聚合点， The proposed method optimizes the path of sensor nodes by using self-organizing mapping(SOM). We combine the optimized path graphics and the K-means algorithm to find the path internal aggregation point.