Canon Kabushiki Kaisha

Tsewei Chen

Masami Kato

Dongchao Wen

Wei Tao

Junjie Liu

Hongxing Gao

Motoki Yoshinaga

Shiori Wakino

Masahiro Ariizumi

Daisuke Nakashima

Deyu Wang

Kotaro Kitajima

Shunta Tate

Makoto Kimura

Naoko Watazawa

Qin Yang

Xuyu Zhao

Yusuke Yachide

Qiao Wang

Tadayuki Ito

Yutaka Murata

A training and application method, apparatus, system and storage medium of a neural network model is provided. The training method comprises: determining a constraint threshold range according to the number of training iterations and a calculation accuracy of the neural network model, and constraining a gradient of a weight to be within the constraint threshold range, so that when the gradient of a low-accuracy weight is distorted due to a quantization error, the distortion of the gradient is corrected by the constraint of the gradient, thereby making the trained network model achieve the expected performance.

Training and application method apparatus system and storage medium of neural network model

An apparatus includes memories hold feature planes each corresponding to a corresponding layer of layers in a neural network, a calculation unit performs calculation processing on the feature planes, and a memory control unit reads a feature plane from any of the memories and input the feature plane to the calculation unit, and writes a feature plane output from the calculation unit to any of the memories. In a case where feature planes corresponding to different layers are connected and the calculation processing is performed, the memory control unit writes the feature planes to be connected, in memories other than a specific memory among the memories, reads the feature planes to be connected, from the memories other than the specific memory and inputs the feature planes to the calculation unit, and writes the feature plane output from the calculation unit in the specific memory.

Calculation processing apparatus and calculation processing method

An information processing apparatus comprises a conversion unit configured to, if a bit width of a weight coefficient in a first layer in weight coefficients in a plurality of layers is N bits, wherein N is a predetermined integer not less than 2, output the weight coefficient, and if the bit width of the weight coefficient is not N bits, output a weight coefficient of N bits obtained by converting the weight coefficient, and an operation unit configured to generate a feature image in a second layer based on a multiply-accumulate operation between the weight coefficient output by the conversion unit and a feature image in the first layer.

Information processing apparatus, information processing method, and non-transitory computer-readable storage medium

An apparatus for performing filter processing on a data array in a processing target block of a predetermined size is provided. A data memory holds the data array in the processing target block. A coefficient memory holds weight coefficients of a filter used for the filter processing. A controller determines, in a determination, whether data in a reference region in the processing target block, set in correspondence with the processing target block, are zero values. A processor generates a convolution operation result of the weight coefficients and data at a plurality of positions in the processing target block. The controller controls, based on a result of the determination, whether to perform at least some of multiply-accumulate operations of the data and the weight coefficients when the processor generates the convolution operation result.

Apparatus for performing filter processing using convolution operation, method of performing filter processing, and medium

An apparatus sets coefficients in a first array based on first information indicating an image capturing orientation of a first image, generates a first map by applying the coefficients to the first image, acquires a template feature corresponding to an object based on the first map, registers the template feature in an array based on the first information, sets coefficients in a second array based on second information indicating an image capturing orientation of a second image, generates a second map by applying the coefficients set in the second array to the second image, sets the template feature in a feature array based on the second information, performs a correlation calculation between the template feature set in the feature array and the second map, and detects the object from the second image based on a result of the correlation calculation.

Image processing apparatus, image processing method, and non-transitory computer-readable storage medium

The present disclosure discloses an apparatus and a method for detecting a facial pose, an image processing system, and a storage medium. The apparatus comprises: an obtaining unit to obtain at least three keypoints of at least one face from an input image based on a pre-generated neural network, wherein coordinates of the keypoints obtained via a layer in the neural network for obtaining coordinates are three-dimensional coordinates; and a determining unit to determine, for the at least one face, a pose of the face based on the obtained keypoints, wherein the determined facial pose includes at least an angle. According to the present disclosure, the accuracy of the three-dimensional coordinates of the facial keypoints can be improved, thus the detection precision of a facial pose can be improved.

Apparatus and method for detecting facial pose, image processing system, and storage medium

The present disclosure provides a training and application method of a multi-layer neural network model, apparatus and a storage medium. In a forward propagation of the multi-layer neural network model, the number of input feature maps is expanded and a data computation is performed by using the expanded input feature maps.

Training and application method of a multi-layer neural network model, apparatus and storage medium

There is provided with a data processing apparatus for detecting an object from an image using a hierarchical neural network. The data processing apparatus has parallel first and second neural networks. An obtaining unit obtains a table which defines different first and second portions. An operation unit performs calculation of the feature data of a third portion based on feature data of the first portion identified using the table and on a weighting parameter between first and second layers of the first neural network, and calculation of feature data of a fourth portion based on feature data of the second portion identified using the table and on a weighting parameter between the first and second layers of the second neural network.

Data processing apparatus, training apparatus, method of detecting an object, method of training, and medium

There is provided with a processing apparatus. A data holder holds at least some of data of a plurality of channels in a target layer among a plurality of layers. Each of a plurality of processors performs, in parallel, a product-sum operation using the data of one channel of the target layer and a coefficient corresponding to the target layer. A selector selects whether to perform first processing or second processing on the basis of information specifying processing in the target layer. The first processing includes inputting the data of one channel of the target layer into one of the plurality of processors. The second processing includes inputting the data of one channel of the target layer to the plurality of processors in parallel.

Processing apparatus for performing processing using a convolutional neural network

A data processing apparatus for executing data processing using a neural network including a plurality of hierarchal levels includes an extraction unit configured to extract intermediate feature data from input feature data, a calculation unit configured to calculate output feature data by reducing the number of channels of the intermediate feature data, a storage unit configured to store the output feature data calculated by the calculation unit and provide the input feature data to the extraction unit, and a control unit configured to control the number of channels of the intermediate feature data to be extracted by the extraction unit and the number of channels of the output feature data to be calculated by the calculation unit.

Data processing apparatus and data processing method for executing processing using neural network

The present disclosure provides a training and application method of a multi-layer neural network model, apparatus and storage medium. A number of channels of a filter in at least one convolutional layer in the multi-layer neural network model is expanded, and a convolution computation is performed by using the filter after expanding the number of channels, so that the performance of the network model does not degrade while simplifying the network model.

There is provided with a filter processing device for performing a convolution operation using a filter on a feature plane. An acquisition unit acquires feature plane data, filter coefficients of the filter, and valid coefficient information which specifies filter coefficients to be used in the convolution operation among the filter coefficients of the filter. A calculation unit performs a convolution operation of the filter coefficients specified by the valid coefficient information and pieces of the feature plane data corresponding to the specified filter coefficients.

Filter processing device and method of performing convolution operation at filter processing device

An information processing apparatus comprises a control unit configured to set a shift amount based on a bit width of data, for each layer of a network including a plurality of layers, a plurality of MAC (multiply-accumulate) units configured to execute MAC operations on a plurality of data and a plurality of filter coefficients of the layer, a plurality of shift operation units configured to shift a plurality of MAC operation results obtained by the plurality of MAC units based on the shift amount, and an adding unit configured to calculate a total sum of the plurality of MAC operation results shifted by the plurality of shift operation units.

Information processing apparatus, information processing method, non-transitory computer-readable storage medium

An apparatus includes a storage control unit that divides, into two-dimensional blocks, a feature image of a layer and stores the respective blocks in any one of a predetermined number of memories, a unit that determines a pattern for reading blocks from the memories based on information relating to an operation on the feature image, and a unit that reads blocks from the memories in accordance with the pattern. The storage control unit assigns repeatedly, for the two-dimensional blocks, the memories in a predetermined order from a head row/column along a row/column. In a second or a subsequent row/column, a memory, which the assignment is started, is shifted by a constant number from a previous row/column in the predetermined order.

Image processing apparatus, imaging apparatus, image processing method, non-transitory computer-readable storage medium

In order to perform a discrimination calculation using a small-capacity storage unit, a discrimination calculation apparatus comprises a feature calculation unit configured to sequentially calculate a feature of discrimination target data for each hierarchical layer, a discrimination calculation unit configured to sequentially perform a partial discrimination calculation on the discrimination target data using the feature sequentially calculated by the feature calculation unit and store a result of the partial discrimination calculation in a discrimination result storage unit, and a control unit configured to control the discrimination calculation unit to perform a next partial discrimination calculation using the feature sequentially calculated by the feature calculation unit and the result of the partial discrimination calculation stored in the discrimination result storage unit and to store a result of the next partial discrimination calculation in the discrimination result storage unit.

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Tokyo, Japan

Tsewei Chen