It is well known that deep learning depends on a large amount of clean data.Because of high annotation cost,various methods have been devoted to annotating the data automatically.However,a larger number of the noisy l...It is well known that deep learning depends on a large amount of clean data.Because of high annotation cost,various methods have been devoted to annotating the data automatically.However,a larger number of the noisy labels are generated in the datasets,which is a challenging problem.In this paper,we propose a new method for selecting training data accurately.Specifically,our approach fits a mixture model to the per-sample loss of the raw label and the predicted label,and the mixture model is utilized to dynamically divide the training set into a correctly labeled set,a correctly predicted set,and a wrong set.Then,a network is trained with these sets in the supervised learning manner.Due to the confirmation bias problem,we train the two networks alternately,and each network establishes the data division to teach the other network.When optimizing network parameters,the labels of the samples fuse respectively by the probabilities from the mixture model.Experiments on CIFAR-10,CIFAR-100 and Clothing1M demonstrate that this method is the same or superior to the state-of-the-art methods.展开更多
Existing algorithms of dish recognition mainly focus on accuracy with predefined classes,thus limiting their application scope.In this paper,we propose a practical two-stage dish recognition framework(DRNet)that yield...Existing algorithms of dish recognition mainly focus on accuracy with predefined classes,thus limiting their application scope.In this paper,we propose a practical two-stage dish recognition framework(DRNet)that yields a tradeoff between speed and accuracy while adapting to the variation in class numbers.In the first stage,we build an arbitrary-oriented dish detector(AODD)to localize dish position,which can effectively alleviate the impact of background noise and pose variations.In the second stage,we propose a dish reidentifier(DReID)to recognize the registered dishes to handle uncertain categories.To further improve the accuracy of DRNet,we design an attribute recognition(AR)module to predict the attributes of dishes.The attributes are used as auxiliary information to enhance the discriminative ability of DRNet.Moreover,pruning and quantization are processed on our model to be deployed in embedded environments.Finally,to facilitate the study of dish recognition,a well-annotated dataset is established.Our AODD,DReID,AR,and DRNet run at about 14,25,16,and 5 fps on the hardware RKNN 3399 pro,respectively.展开更多
基金supported by SRC-Open Project of Research Center of Security Video and Image Processing Engineering Technology of Guizhou ([2020]001)Beijing Advanced Innovation Center for Intelligent Robots and Systems (2018IRS20)National Natural Science Foundation of China (Grant No.61973334).
文摘It is well known that deep learning depends on a large amount of clean data.Because of high annotation cost,various methods have been devoted to annotating the data automatically.However,a larger number of the noisy labels are generated in the datasets,which is a challenging problem.In this paper,we propose a new method for selecting training data accurately.Specifically,our approach fits a mixture model to the per-sample loss of the raw label and the predicted label,and the mixture model is utilized to dynamically divide the training set into a correctly labeled set,a correctly predicted set,and a wrong set.Then,a network is trained with these sets in the supervised learning manner.Due to the confirmation bias problem,we train the two networks alternately,and each network establishes the data division to teach the other network.When optimizing network parameters,the labels of the samples fuse respectively by the probabilities from the mixture model.Experiments on CIFAR-10,CIFAR-100 and Clothing1M demonstrate that this method is the same or superior to the state-of-the-art methods.
基金the National Natural Science Foundation of China(Grant Nos.61972167 and 61802135)the Project of Guangxi Science and Technology(Grant No.GuiKeAD21075030)+3 种基金the Guangxi“Bagui Scholar”Teams for Innovation and Research Projectthe Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processingthe Guangxi Talent Highland Project of Big Data Intelligence and Applicationthe Open Project Program of the National Laboratory of Pattern Recognition(NLPR)(Grant No.202000012)。
文摘Existing algorithms of dish recognition mainly focus on accuracy with predefined classes,thus limiting their application scope.In this paper,we propose a practical two-stage dish recognition framework(DRNet)that yields a tradeoff between speed and accuracy while adapting to the variation in class numbers.In the first stage,we build an arbitrary-oriented dish detector(AODD)to localize dish position,which can effectively alleviate the impact of background noise and pose variations.In the second stage,we propose a dish reidentifier(DReID)to recognize the registered dishes to handle uncertain categories.To further improve the accuracy of DRNet,we design an attribute recognition(AR)module to predict the attributes of dishes.The attributes are used as auxiliary information to enhance the discriminative ability of DRNet.Moreover,pruning and quantization are processed on our model to be deployed in embedded environments.Finally,to facilitate the study of dish recognition,a well-annotated dataset is established.Our AODD,DReID,AR,and DRNet run at about 14,25,16,and 5 fps on the hardware RKNN 3399 pro,respectively.
