Google旗下的人工智能研究组织DeepMind，发布了最新的机器人物体堆栈任务RGB-Stacking，其提供基于计算机视觉技术的机器人一个测试基准，在这个基准测试中，机器人需要学习抓住不同物体，并且使物体保持平衡地堆栈在一起。DeepMind希望借由这个基准测试，促进机器人的研究，增进机器人操作物体的能力。研究人员提到，与之前的研...
Google旗下的人工智能研究组织DeepMind,发布了最新的机器人物体堆栈任务RGB-Stacking,其提供基于计算机视觉技术的机器人一个测试基准,在这个基准测试中,机器人需要学习抓住不同物体,并且使物体保持平衡地堆栈在一起。DeepMind希望借由这个基准测试,促进机器人的研究,增进机器人操作物体的能力。 研究人员提到,与之前的研究不...
The current state-of-the-art on RGB-Stacking is TAPIR (MOVi-E). See a full comparison of 2 papers with code.
本发明公开了一种基于RGB图像和Stacking多模型融合的恶意软件家族分类方法,系统和介质,方法包括:构造API类别数据库Q;提取恶意软件API调用顺序链;根据API调用顺序链构造API调用关系对,得到API调用关系对有向图G;使用最大熵模型中的改进的迭代尺度算法进行权重的确定,得到每个API调用关系对的权重wi,j;得到代表恶意软件...
The current state-of-the-art on TAP-Vid-RGB-Stacking is BootsTAPIR. See a full comparison of 2 papers with code.
LAB-Voting, which uses the LAB color space, improves the accuracy to 98.49%, outperforming RGB approaches and the performance of Bueno et al.’s study and approaching the best results. RGB-Stacking shows a performance of 97.06%, which is also relatively high but still lower than that of LAB...
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本发明公开了一种基于RGB图像和Stacking多模型融合的恶意软件家族分类方法、系统和介质,方法包括:构造API类别数据库Q;提取恶意软件API调用顺序链;根据API调用顺序链构造API调用关系对,得到API调用关系对有向图G;使用最大熵模型中的改进的迭代尺度算法进行权重的确定,得到每个API调用关系对的权重wi,j;得到代表恶意软件调...
To tackle those problems, we propose a novel pose estimation method for industrial stacking scenes based on RGB images. Specifically, due to the common symmetry characteristics of industrial workpieces, we firstly present a new standardized spatial representation method, which could auto-encode the 2D...
The results demonstrated that the combination of algorithms produced superior performance, especially in the LAB color space, where voting scored 98.49% and stacking 98.71%. In comparison, in the RGB color space, voting scored 96.59% and stacking 97.06%. These results surpass those generally ...