In recent years,skeleton-based action recognition has made great achievements in Computer Vision.A graph convolutional network(GCN)is effective for action recognition,modelling the human skeleton as a spatio-temporal ...In recent years,skeleton-based action recognition has made great achievements in Computer Vision.A graph convolutional network(GCN)is effective for action recognition,modelling the human skeleton as a spatio-temporal graph.Most GCNs define the graph topology by physical relations of the human joints.However,this predefined graph ignores the spatial relationship between non-adjacent joint pairs in special actions and the behavior dependence between joint pairs,resulting in a low recognition rate for specific actions with implicit correlation between joint pairs.In addition,existing methods ignore the trend correlation between adjacent frames within an action and context clues,leading to erroneous action recognition with similar poses.Therefore,this study proposes a learnable GCN based on behavior dependence,which considers implicit joint correlation by constructing a dynamic learnable graph with extraction of specific behavior dependence of joint pairs.By using the weight relationship between the joint pairs,an adaptive model is constructed.It also designs a self-attention module to obtain their inter-frame topological relationship for exploring the context of actions.Combining the shared topology and the multi-head self-attention map,the module obtains the context-based clue topology to update the dynamic graph convolution,achieving accurate recognition of different actions with similar poses.Detailed experiments on public datasets demonstrate that the proposed method achieves better results and realizes higher quality representation of actions under various evaluation protocols compared to state-of-the-art methods.展开更多
为了对矿业城市的土地利用情景进行预测,该文以典型矿业城市武安市为例,将GIS技术和CLUE-S(conversion of land use and its effects at small regional extent)模型应用到武安市土地利用变化情景模拟研究中,通过土地利用结构变化、矿...为了对矿业城市的土地利用情景进行预测,该文以典型矿业城市武安市为例,将GIS技术和CLUE-S(conversion of land use and its effects at small regional extent)模型应用到武安市土地利用变化情景模拟研究中,通过土地利用结构变化、矿业城市土地利用空间分布和驱动因子的定量关系对武安市土地利用变化进行相应约束,设计了趋势发展情景、耕地保护情景、生态安全情景3种模式,生成2020年不同情景方案下土地利用预测图,并对预测结果进行比较分析。研究结果表明:在趋势发展情景下,林地、建筑用地呈现上升趋势,体现了经济发展和环境保护双管齐下的成效,这也与实际情况相吻合;耕地保护情景下,耕地分布制约了建设用地的适度扩张;生态安全情景下,受生态环境政策影响,林地增长趋势明显,工矿用地急剧减少。综合考虑到武安市社会、经济、生态以及耕地保护等多方面的协调发展,研究认为趋势发展情景更为合理,其他2种情景可为趋势发展情景进行适度的修正和补充。该研究为区域土地资源的优化配置提供决策依据,同时研究结果也进一步验证了CLUE-S模型能够较好地模拟预测不同约束条件下矿业城市土地利用空间变化。展开更多
基金supported in part by the 2023 Key Supported Project of the 14th Five Year Plan for Education and Science in Hunan Province with No.ND230795.
文摘In recent years,skeleton-based action recognition has made great achievements in Computer Vision.A graph convolutional network(GCN)is effective for action recognition,modelling the human skeleton as a spatio-temporal graph.Most GCNs define the graph topology by physical relations of the human joints.However,this predefined graph ignores the spatial relationship between non-adjacent joint pairs in special actions and the behavior dependence between joint pairs,resulting in a low recognition rate for specific actions with implicit correlation between joint pairs.In addition,existing methods ignore the trend correlation between adjacent frames within an action and context clues,leading to erroneous action recognition with similar poses.Therefore,this study proposes a learnable GCN based on behavior dependence,which considers implicit joint correlation by constructing a dynamic learnable graph with extraction of specific behavior dependence of joint pairs.By using the weight relationship between the joint pairs,an adaptive model is constructed.It also designs a self-attention module to obtain their inter-frame topological relationship for exploring the context of actions.Combining the shared topology and the multi-head self-attention map,the module obtains the context-based clue topology to update the dynamic graph convolution,achieving accurate recognition of different actions with similar poses.Detailed experiments on public datasets demonstrate that the proposed method achieves better results and realizes higher quality representation of actions under various evaluation protocols compared to state-of-the-art methods.
文摘为了对矿业城市的土地利用情景进行预测,该文以典型矿业城市武安市为例,将GIS技术和CLUE-S(conversion of land use and its effects at small regional extent)模型应用到武安市土地利用变化情景模拟研究中,通过土地利用结构变化、矿业城市土地利用空间分布和驱动因子的定量关系对武安市土地利用变化进行相应约束,设计了趋势发展情景、耕地保护情景、生态安全情景3种模式,生成2020年不同情景方案下土地利用预测图,并对预测结果进行比较分析。研究结果表明:在趋势发展情景下,林地、建筑用地呈现上升趋势,体现了经济发展和环境保护双管齐下的成效,这也与实际情况相吻合;耕地保护情景下,耕地分布制约了建设用地的适度扩张;生态安全情景下,受生态环境政策影响,林地增长趋势明显,工矿用地急剧减少。综合考虑到武安市社会、经济、生态以及耕地保护等多方面的协调发展,研究认为趋势发展情景更为合理,其他2种情景可为趋势发展情景进行适度的修正和补充。该研究为区域土地资源的优化配置提供决策依据,同时研究结果也进一步验证了CLUE-S模型能够较好地模拟预测不同约束条件下矿业城市土地利用空间变化。
