针对拥挤场景下的尺度变化导致人群计数任务中精度较低的问题,提出一种基于多尺度注意力网络(MANet)的密集人群计数模型。通过构建多列模型以捕获多尺度特征,促进尺度信息融合;使用双注意力模块获取上下文依赖关系,增强多尺度特征图的信...针对拥挤场景下的尺度变化导致人群计数任务中精度较低的问题,提出一种基于多尺度注意力网络(MANet)的密集人群计数模型。通过构建多列模型以捕获多尺度特征,促进尺度信息融合;使用双注意力模块获取上下文依赖关系,增强多尺度特征图的信息;采用密集连接重用多尺度特征图,生成高质量的密度图,之后对密度图积分得到计数。此外,提出一种新的损失函数,直接使用点注释图进行训练,以减少由高斯滤波生成新的密度图而带来的额外的误差。在公开人群数据集ShanghaiTech Part A/B、UCF-CC-50、UCF-QNRF上的实验结果均达到了最优,表明该网络可以有效处理拥挤场景下的目标多尺度,并且生成高质量的密度图。展开更多
木材缺陷智能检测技术可以有效降低人工误检带来的经济损失,对提高木材加工智能化水平具有重要意义。提出了一种木材缺陷智能检测算法,通过双循环生成对抗网络(double least generative adversarial networks,DLGAN)及密集卷积网络(Dens...木材缺陷智能检测技术可以有效降低人工误检带来的经济损失,对提高木材加工智能化水平具有重要意义。提出了一种木材缺陷智能检测算法,通过双循环生成对抗网络(double least generative adversarial networks,DLGAN)及密集卷积网络(Dense-Net)来检测色差、虫眼、裂纹、节子和伤疤等5种木材常见缺陷。首先,使用DLGAN技术扩充数据集,提高数据集的多样性和数量,缓解了因训练数据不足而导致的过拟合问题;其次,基于Dense-Net的特点,采用密集的卷积块序列提高对微弱特征的提取和学习能力,以便更好地检测木材缺陷。试验结果表明,相比VGG16、Inception-v2、ResNet 3种经典卷积神经网络,基于DLGAN增广数据集训练的Dense-Net模型有效提高了木材缺陷检测模型的性能,平均准确率达到92.7%,在只使用少量训练数据的情况下模型依然具有良好的图像生成能力和训练鲁棒性。展开更多
Dense captioning aims to simultaneously localize and describe regions-of-interest(RoIs)in images in natural language.Specifically,we identify three key problems:1)dense and highly overlapping RoIs,making accurate loca...Dense captioning aims to simultaneously localize and describe regions-of-interest(RoIs)in images in natural language.Specifically,we identify three key problems:1)dense and highly overlapping RoIs,making accurate localization of each target region challenging;2)some visually ambiguous target regions which are hard to recognize each of them just by appearance;3)an extremely deep image representation which is of central importance for visual recognition.To tackle these three challenges,we propose a novel end-to-end dense captioning framework consisting of a joint localization module,a contextual reasoning module and a deep convolutional neural network(CNN).We also evaluate five deep CNN structures to explore the benefits of each.Extensive experiments on visual genome(VG)dataset demonstrate the effectiveness of our approach,which compares favorably with the state-of-the-art methods.展开更多
文摘针对拥挤场景下的尺度变化导致人群计数任务中精度较低的问题,提出一种基于多尺度注意力网络(MANet)的密集人群计数模型。通过构建多列模型以捕获多尺度特征,促进尺度信息融合;使用双注意力模块获取上下文依赖关系,增强多尺度特征图的信息;采用密集连接重用多尺度特征图,生成高质量的密度图,之后对密度图积分得到计数。此外,提出一种新的损失函数,直接使用点注释图进行训练,以减少由高斯滤波生成新的密度图而带来的额外的误差。在公开人群数据集ShanghaiTech Part A/B、UCF-CC-50、UCF-QNRF上的实验结果均达到了最优,表明该网络可以有效处理拥挤场景下的目标多尺度,并且生成高质量的密度图。
基金Project(2020A1515010718)supported by the Basic and Applied Basic Research Foundation of Guangdong Province,China。
文摘Dense captioning aims to simultaneously localize and describe regions-of-interest(RoIs)in images in natural language.Specifically,we identify three key problems:1)dense and highly overlapping RoIs,making accurate localization of each target region challenging;2)some visually ambiguous target regions which are hard to recognize each of them just by appearance;3)an extremely deep image representation which is of central importance for visual recognition.To tackle these three challenges,we propose a novel end-to-end dense captioning framework consisting of a joint localization module,a contextual reasoning module and a deep convolutional neural network(CNN).We also evaluate five deep CNN structures to explore the benefits of each.Extensive experiments on visual genome(VG)dataset demonstrate the effectiveness of our approach,which compares favorably with the state-of-the-art methods.
