Face attribute classification(FAC)is a high-profile problem in biometric verification and face retrieval.Although recent research has been devoted to extracting more delicate image attribute features and exploiting th...Face attribute classification(FAC)is a high-profile problem in biometric verification and face retrieval.Although recent research has been devoted to extracting more delicate image attribute features and exploiting the inter-attribute correlations,significant challenges still remain.Wavelet scattering transform(WST)is a promising non-learned feature extractor.It has been shown to yield more discriminative representations and outperforms the learned representations in certain tasks.Applied to the image classification task,WST can enhance subtle image texture information and create local deformation stability.This paper designs a scattering-based hybrid block,to incorporate frequency-domain(WST)and image-domain features in a channel attention manner(Squeezeand-Excitation,SE),termed WS-SE block.Compared with CNN,WS-SE achieves a more efficient FAC performance and compensates for the model sensitivity of the small-scale affine transform.In addition,to further exploit the relationships among the attribute labels,we propose a learning strategy from a causal view.The cause attributes defined using the causalityrelated information can be utilized to infer the effect attributes with a high confidence level.Ablative analysis experiments demonstrate the effectiveness of our model,and our hybrid model obtains state-of-the-art results in two public datasets.展开更多
At first, we briefly introduced fuzzy sets and related concepts of meteorology and meteorological administrative power to build the basic theoretical framework of the meteorological administrative power. Then, the att...At first, we briefly introduced fuzzy sets and related concepts of meteorology and meteorological administrative power to build the basic theoretical framework of the meteorological administrative power. Then, the attributes of meteorological administrative power were analyzed, espe- cially implementation and functions, and it was divided into five types like supervisory power for meteorological forecast. Finally, meteorological ad- ministrative power＇s expansion and control methods were discussed. It acts as the research foundation in practice to improve the meteorological ad- ministrative law enforcement and achieve meteorological public interest better.展开更多
基金supported by the National Key Research and Development Project of China(Grant No.2018AAA0100802)Opening Foundation of National Engineering Laboratory for Intelligent Video Analysis and Application,and Experimental Center of Artificial Intelligence of Beijing Normal University.
文摘Face attribute classification(FAC)is a high-profile problem in biometric verification and face retrieval.Although recent research has been devoted to extracting more delicate image attribute features and exploiting the inter-attribute correlations,significant challenges still remain.Wavelet scattering transform(WST)is a promising non-learned feature extractor.It has been shown to yield more discriminative representations and outperforms the learned representations in certain tasks.Applied to the image classification task,WST can enhance subtle image texture information and create local deformation stability.This paper designs a scattering-based hybrid block,to incorporate frequency-domain(WST)and image-domain features in a channel attention manner(Squeezeand-Excitation,SE),termed WS-SE block.Compared with CNN,WS-SE achieves a more efficient FAC performance and compensates for the model sensitivity of the small-scale affine transform.In addition,to further exploit the relationships among the attribute labels,we propose a learning strategy from a causal view.The cause attributes defined using the causalityrelated information can be utilized to infer the effect attributes with a high confidence level.Ablative analysis experiments demonstrate the effectiveness of our model,and our hybrid model obtains state-of-the-art results in two public datasets.
基金Supported by the Research Fund of Southwest University(SWU1209301)Humanities and Social sciences Research Fund of Chongqing Municipal Education Commission (RWSK-CQJY201101)
文摘At first, we briefly introduced fuzzy sets and related concepts of meteorology and meteorological administrative power to build the basic theoretical framework of the meteorological administrative power. Then, the attributes of meteorological administrative power were analyzed, espe- cially implementation and functions, and it was divided into five types like supervisory power for meteorological forecast. Finally, meteorological ad- ministrative power＇s expansion and control methods were discussed. It acts as the research foundation in practice to improve the meteorological ad- ministrative law enforcement and achieve meteorological public interest better.
