Abstract: Provided are method and apparatus for image processing. A neural network includes an encoding network, an intermediate network, and a decoding network including multiple input layers and an output layer. In the method, at an input layer of the decoding network, first output data is received from a previous layer, and a first operation is performed on the first output data to obtain first input data of the input layer, the input layer is any one of multiple input layers. At the input layer, second output data is received from a corresponding layer of the encoding network, and a second operation is performed on the second output data to obtain second input data of the input layer. Output data of the input layer is obtained according to the first and second input data. Operations are performed in a next layer based on the output data to obtain an output image.

Abstract: Provided are a method and apparatus for image processing and a terminal. In the method, an input image is received and processed in a neural network to obtain an output image according to global information of the input image. The terminal includes at least one processor; and a memory coupled with the at least one processor and configured to store instructions which, when executed by the at least one processor, are operable with the at least one processor to implement a neural network to receive an input image and process the input image in the neural network to obtain an output image according to global information of the input image.

Abstract: A device and process for electrochemical treatment of wastewater with a screening current collector-based flow anode are provided. The device includes: a shell, where an interior of the shell is a cavity structure, and the shell is provided with a water inlet and a water outlet; a screening current collector structure, where the screening current collector structure includes a screening anode current collector and a cathode current collector, a cathode/anode separator is provided between the screening anode current collector and the cathode current collector, and the screening anode current collector and the cathode current collector each are connected to an external circuit; an anode cell and a cathode cell, where the anode cell and the cathode cell are formed through division of the cavity structure, and an electrolyte is provided in each of the anode cell and the cathode cell; and a flow anode suspended in the anode cell.

Abstract: A method includes receiving a facial image; receiving a facial image; and obtaining, using a multi-task convolutional neural network, a detected face location and a facial characteristic category set of a plurality of first facial characteristic categories; selecting a first face alignment model from a plurality of face alignment models based on the facial characteristic category set; and obtaining, using the first face alignment model, a plurality of facial landmarks. The first facial characteristic categories are arranged hierarchically. A hierarchy of the first facial characteristic categories includes a plurality of first levels corresponding to a plurality of corresponding facial characteristics. The facial characteristic category set includes the first facial characteristic categories of a path of the hierarchy of the first facial characteristic categories.

Abstract: An optimization method for a directional preparation technique and efficient use of semi-coke for blast furnace injection. Firstly, the volatile and the ash content of target semi-coke are preset, and then the volatile and the ash removal percentages of a raw coal are calculated; after ash removal, several sets of dry distillation carbonization temperatures and carbonization times are obtained according to the volatile removal percentage, and the relationships between a combustion rate, abrasiveness, explosiveness and jet flow property and the carbonization temperature are respectively established to obtain the optimal actual carbonization temperature; and semi-coke for blast furnace injection is obtained at an actual carbonization temperature.

Abstract: Provided are method and apparatus for image processing. A neural network includes an encoding network, an intermediate network, and a decoding network including multiple input layers and an output layer. In the method, at an input layer of the decoding network, first output data is received from a previous layer, and a first operation is performed on the first output data to obtain first input data of the input layer, the input layer is any one of multiple input layers. At the input layer, second output data is received from a corresponding layer of the encoding network, and a second operation is performed on the second output data to obtain second input data of the input layer. Output data of the input layer is obtained according to the first and second input data. Operations are performed in a next layer based on the output data to obtain an output image.

Abstract: Provided are a method and apparatus for image processing and a terminal. In the method, an input image is received and processed in a neural network to obtain an output image according to global information of the input image. The terminal includes at least one processor; and a memory coupled with the at least one processor and configured to store instructions which, when executed by the at least one processor, are operable with the at least one processor to implement a neural network to receive an input image and process the input image in the neural network to obtain an output image according to global information of the input image.

Abstract: A method includes receiving a facial image; receiving a facial image; and obtaining, using a multi-task convolutional neural network, a detected face location and a facial characteristic category set of a plurality of first facial characteristic categories; selecting a first face alignment model from a plurality of face alignment models based on the facial characteristic category set; and obtaining, using the first face alignment model, a plurality of facial landmarks. The first facial characteristic categories are arranged hierarchically. A hierarchy of the first facial characteristic categories includes a plurality of first levels corresponding to a plurality of corresponding facial characteristics.

Abstract: A method includes: receiving a facial image (204); obtaining a facial shape (206) using the facial image (204); defining, using the facial image (204) and the facial shape (206), a plurality of facial component-specific local regions, wherein each of the facial component-specific local regions includes a corresponding separately considered facial component of a plurality of separately considered facial components from the facial image (204), and the corresponding separately considered facial component of the separately considered facial components corresponds to a corresponding first facial landmark set (208) of a plurality of first facial landmark sets in the facial shape (206); for each of the facial component-specific local regions, performing a cascaded regression method using each of the facial component-specific local regions and a corresponding facial landmark set (208) of the first facial landmark sets to obtain a corresponding facial landmark set (210) of a plurality of second facial landmark sets.