Weakly supervised object localization mines the pixel-level location information based on image-level annotations.The traditional weakly supervised object localization approaches exploit the last convolutional feature...Weakly supervised object localization mines the pixel-level location information based on image-level annotations.The traditional weakly supervised object localization approaches exploit the last convolutional feature map to locate the discriminative regions with abundant semantics.Although it shows the localization ability of classification network,the process lacks the use of shallow edge and texture features,which cannot meet the requirement of object integrity in the localization task.Thus,we propose a novel shallow feature-driven dual-edges localization(DEL)network,in which dual kinds of shallow edges are utilized to mine entire target object regions.Specifically,we design an edge feature mining(EFM)module to extract the shallow edge details through the similarity measurement between the original class activation map and shallow features.We exploit the EFM module to extract two kinds of edges,named the edge of the shallow feature map and the edge of shallow gradients,for enhancing the edge details of the target object in the last convolutional feature map.The total process is proposed during the inference stage,which does not bring extra training costs.Extensive experiments on both the ILSVRC and CUB-200-2011 datasets show that the DEL method obtains consistency and substantial performance improvements compared with the existing methods.展开更多
在多机器人同时定位与地图创建(Simultaneous Localization and Mapping,SLAM)协同工作下,要求融合各机器人的特征子地图形成单一的公共地图,利用三角形相似性原理,实现SLAM定位中各机器人子地图的相互匹配。在机器人创建的地图中,依据...在多机器人同时定位与地图创建(Simultaneous Localization and Mapping,SLAM)协同工作下,要求融合各机器人的特征子地图形成单一的公共地图,利用三角形相似性原理,实现SLAM定位中各机器人子地图的相互匹配。在机器人创建的地图中,依据路标位置相关的特征组成最小三角形,并通过三角形相似性原理对各机器人创建子地图进行相似性匹配,并记录相似三角形对应点匹配次数,最后彼此匹配次数最多的对应路标即为相关联的路标对。实验结果表明该方法是有效的,且鲁棒性强。展开更多
基金This work was partly supported by National Natural Science Foundation of China(No.62072394)Natural Science Foundation of Hebei Province,China(No.F2021203019)Hebei Key Laboratory Project,China(No.202250701010046).
文摘Weakly supervised object localization mines the pixel-level location information based on image-level annotations.The traditional weakly supervised object localization approaches exploit the last convolutional feature map to locate the discriminative regions with abundant semantics.Although it shows the localization ability of classification network,the process lacks the use of shallow edge and texture features,which cannot meet the requirement of object integrity in the localization task.Thus,we propose a novel shallow feature-driven dual-edges localization(DEL)network,in which dual kinds of shallow edges are utilized to mine entire target object regions.Specifically,we design an edge feature mining(EFM)module to extract the shallow edge details through the similarity measurement between the original class activation map and shallow features.We exploit the EFM module to extract two kinds of edges,named the edge of the shallow feature map and the edge of shallow gradients,for enhancing the edge details of the target object in the last convolutional feature map.The total process is proposed during the inference stage,which does not bring extra training costs.Extensive experiments on both the ILSVRC and CUB-200-2011 datasets show that the DEL method obtains consistency and substantial performance improvements compared with the existing methods.
文摘在多机器人同时定位与地图创建(Simultaneous Localization and Mapping,SLAM)协同工作下,要求融合各机器人的特征子地图形成单一的公共地图,利用三角形相似性原理,实现SLAM定位中各机器人子地图的相互匹配。在机器人创建的地图中,依据路标位置相关的特征组成最小三角形,并通过三角形相似性原理对各机器人创建子地图进行相似性匹配,并记录相似三角形对应点匹配次数,最后彼此匹配次数最多的对应路标即为相关联的路标对。实验结果表明该方法是有效的,且鲁棒性强。
