Abstract: A method for cooling a driving motor with a locked rotor of a vehicle is disclosed. The vehicle includes the driving motor, an engine, an engine end oil pump, and a cooling flow path. The engine is configured to drive the engine end oil pump, and the cooling flow path connects the engine end oil pump to the driving motor. The method includes: in response to determining that a driving mode of the vehicle is an electric mode and that the vehicle meets a locked rotor motor trigger condition, and detecting that a current temperature of the driving motor is greater than an auxiliary cooling starting temperature of the engine, controlling the engine to start to cause the engine end oil pump to drive a coolant in the cooling flow path to cool the driving motor.

Abstract: The present disclosure relates to a method, device and storage medium for improving multi-object tracking. According to an embodiment, the method comprises: performing a split operation on a tracklet provided for one object by a multi-object tracking model. The split operation comprises: determining an appearance feature sequence of the tracklet; determining a clustering label set of the appearance feature sequence; determining an image block label sequence; determining a fragment label sequence corresponding to continuous fragments, having the same clustering labels, in the image block label sequence; in a case where a length of the fragment label sequence is greater than the number of types of the clustering labels in the clustering label set, updating the image block label sequence and the fragment label sequence by performing an update operation; and splitting the tracklet based on the updated image block label sequence. The method may further comprise a merge operation.

Abstract: An information processing device, an information processing method and a computer readable storage medium are disclosed. The information processing device includes: processing circuitry configured to calculate, for each tracklet, a similarity set of each frame of a predetermined frame set included in the tracklet; determine, for each tracklet, a splitting point of the tracklet from the predetermined frame set based on the similarity set; split the tracklet into multiple sub-segments by using the determined splitting point; and merge sub-segments which involve a same object and do not overlap temporally among sub-segments to be merged, to obtain a merged segment, where the sub-segments to be merged include the multiple sub-segments obtained by the splitting unit.

Abstract: The embodiments of the present disclosure provide an apparatus for identifying items, a method for identifying items and an electronic device. The apparatus includes: a detector configured to detect one or more items in a reference area in one or more image frames in video data; a tracker configured to track an item detected in multiple image frames, wherein multi-hierarchy decision is performed on the item in the multiple image frames by using different time windows; and a classifier configured to identify the item according to a decision result of the tracker. Thereby, even if an item is moved briefly in some scenarios, the item will not be identified as two different items, which can reduce a situation in which the item is identified repeatedly and improve accuracy and robustness of item detection.

Abstract: A device and a method for classification using a pre-trained classification model and a computer readable storage medium are provided. The device is configured to extract, for each of multiple images in a target image group to be classified, a feature of the image using a feature extraction layer of the pre-trained classification model; calculate, for each of the multiple images, a contribution of the image to a classification result of the target image group using a contribution calculation layer of the pre-trained classification model; aggregate extracted features of the multiple images based on calculated contributions of the multiple images, to obtain an aggregated feature as a feature of the target image group; and classify the target image group based on the feature of the target image group.

Abstract: A method of training a model, a device of training a model, and an information processing method is provided. The method of training a model comprises: determining a subsample set sequence composed of N subsample sets of a total training sample set; and iteratively training the model in sequence of N stages based on the subsample set sequence; wherein a stage training sample set of a y-th stage from a second stage to a N-th stage of the N stages comprises a y-th subsample set in the subsample set sequence and a downsampled pre-subsample set of a pre-subsample set composed of all subsample sets before the y-th subsample set; and each single class sample quantity of the downsampled pre-subsample set is close to or falls into a single class sample quantity distribution interval of the y-th subsample set.

Abstract: A method and an apparatus for training a neural network, an image recognition method and a computer readable storage medium are disclosed. The neural network includes a first model and a second model. The method for training a neural network includes: acquiring a second image from a first image, wherein a quality of the second image is lower than that of the first image; inputting the first image into the first model of the neural network, and inputting the second image into the second model of the neural network; calculating an attention map and a gradient map of the first model and an attention map and a gradient map of the second model; constructing a loss function based on a matrix of a dot product of the gradient map and the attention map of the first model and a matrix of a dot product of the gradient map and the attention map of the second model; and training the neural network by minimizing the loss function.

Abstract: There are provided a method of knowledge transferring, an information processing apparatus and a storage medium. The method of knowledge transferring includes: obtaining a first model which has been trained in advance with respect to a predetermined task; and training a second model with respect to the predetermined task by utilizing a comprehensive loss function, such that the second model has knowledge of the first model. The comprehensive loss function is based on a first loss function weighted by accuracy of an output result of the first model for a training sample in regard to the predetermined task, and a second loss function. The first loss function represents a difference between processing results of the second model and the first model for the training sample. The second loss function represents accuracy of an output result of the second model for the training sample in regard to the predetermined task.

Abstract: An apparatus for training a classification model includes: a feature extraction unit configured to set, with respect to each training set of a first predetermined number of training sets, feature extraction layers, and extract features of a sample image, where at least two of the training sets at least partially overlap; a feature fusion unit configured to set, with respect to training set, feature fusion layers, and perform a fusion on the extracted features of the sample image; and a loss determination unit configured to set, with respect to training set, a loss determination layer, calculate a loss function of the sample image based on the fused feature of the sample image, and train a classification model based on the loss function. The first predetermined number of training sets share at least one layer of feature fusion layers and feature extraction layers set with respect to each training set.

Abstract: A method for training a classification model includes: performing training on the classification model using first and second sample sets, to calculate a classification loss; extracting a weight vector and a feature vector of each sample; calculating a mean weight vector and a mean feature vector of all samples in the first sample set; calculating a weight loss based on a difference of the weight vector of each sample in the second sample set from the mean weight vector, and calculating a feature loss based on a difference of a feature vector of each sample in the second sample set from the mean feature vector; calculating a total loss of the classification model based on the classification loss and at least one of the feature loss and the weight loss; and adjusting a parameter of the classification model until a predetermined condition is satisfied.

Abstract: An information processing apparatus includes a processor to input each sample image into feature extracting components to obtain at least two features of the sample image, and to cause a classifying component to calculate a classification loss of the sample image based on the at least two features; extract, from each pair of features, a plurality of sample pairs for calculating mutual information between each pair of features; input the plurality of sample pairs into a machine learning architecture corresponding to each pair of features, to calculate an information loss between each pair of features.

Abstract: A device and a method for classification using a pre-trained classification model and a computer readable storage medium are provided. The device is configured to extract, for each of multiple images in a target image group to be classified, a feature of the image using a feature extraction layer of the pre-trained classification model; calculate, for each of the multiple images, a contribution of the image to a classification result of the target image group using a contribution calculation layer of the pre-trained classification model; aggregate extracted features of the multiple images based on calculated contributions of the multiple images, to obtain an aggregated feature as a feature of the target image group; and classify the target image group based on the feature of the target image group.

Abstract: An apparatus for training a classifying model comprises: a first obtaining unit configured to input a sample image to a first machine learning framework, to obtain a first classification probability and a first classification loss; a second obtaining unit configured to input a second image to a second machine learning framework, to obtain a second classification probability and a second classification loss, the two machine learning frameworks having identical structures and sharing identical parameters; a similarity loss calculating unit configured to calculate a similarity loss related to a similarity between the first classification probability and the second classification probability; a total loss calculating unit configured to calculate the sum of the similarity loss, the first classification loss and the second classification loss, as a total loss; and a training unit configured to adjust parameters of the two machine learning frameworks to obtain a trained classifying model.

Abstract: An information processing method includes: inputting sample image into a machine learning architecture to obtain a first feature, and causing a first classifier to calculate a first classification loss; calculating a second feature based on the first feature and a predetermined first mask, and inputting the second feature into the first classifier to calculate an entropy loss; calculating a second mask based on the first mask and the entropy loss to maximize the entropy loss; obtaining an adversarial feature based on the first feature and the second mask, where the adversarial feature is complementary to the second feature; causing, by training the first classifier and the second classifier in association with each other, the second classifier to calculate a second classification loss based on the adversarial feature; and adjusting parameters of the machine learning architecture, the first classifier and the second classifier, to obtain a trained machine learning architecture.

Abstract: The present disclosure relates to an information processing method and an information processing apparatus. The information processing method according to the present disclosure performs training on a classification model by using a plurality of training samples, and comprises the steps of: adjusting a distribution of feature vectors of the plurality of training samples in a feature space based on a typical sample in the plurality of training samples; and performing training on the classification model by using the adjusted feature vectors of the plurality of training samples. Through the technology according to the present disclosure, it is possible to perform pre-adjustment on training samples before training, such that it is possible to reduce discrimination between training samples belonging to a same class and increase discrimination between training samples belonging to different classes in the training process.

Abstract: An apparatus and method for training a classification model and an apparatus for classifying with a classification model are disclosed. The apparatus for training a classification model comprises: a local area obtainment unit to, obtain predetermined local area of each sample image; a feature extraction unit to, with respect to each sample image, set corresponding numbers of feature extraction layers for the global area and each predetermined local area, to extract a global feature of the global area and a local feature of each predetermined local area, wherein the global area and the predetermined local areas share at least one feature extraction layer in which the global feature and each local feature are combined; and a loss determination unit to calculate a loss function of the sample image based on combined features of each sample image, and to train the classification model based on the loss function.

Abstract: The present disclosure relates to an information processing method and an information processing apparatus. The information processing apparatus according to the present disclosure comprises: a determining unit configured to respectively determine a discrimination margin of each class of a plurality of classes of a training sample set containing the plurality of classes relative to other classes; and a training unit configured to use, based on the determined discrimination margin, the training sample set for training a classifying model. By the information processing apparatus and the information processing method according to the present disclosure, a classifying model can be trained by using a training sample set of which training samples are distributed unevenly, so that a classifying model capable of performing accurate classification can be obtained without significantly increasing a calculation cost.

Abstract: A method for training a classification model includes: performing training on the classification model using first and second sample sets, to calculate a classification loss; extracting a weight vector and a feature vector of each sample; calculating a mean weight vector and a mean feature vector of all samples in the first sample set; calculating a weight loss based on a difference of the weight vector of each sample in the second sample set from the mean weight vector, and calculating a feature loss based on a difference of a feature vector of each sample in the second sample set from the mean feature vector; calculating a total loss of the classification model based on the classification loss and at least one of the feature loss and the weight loss; and adjusting a parameter of the classification model until a predetermined condition is satisfied.

Abstract: An information processing device and method, and a device for classifying with a model are provided. The information processing device includes a first training unit being configured to train a first model using a first training sample set, to obtain a trained first model; a second training unit being configured to train the trained first model using a second training sample set while maintaining a predetermined portion of characteristics of the trained first model, to obtain a trained second model, and a third training unit being configured to train a third model using the second training sample set while causing a difference between classification performances of the trained second model and the third model to be within a first predetermined range, to obtain a trained third model as a final model.

Abstract: A method and a device for detecting a hand action are provided. The method includes: identifying an area including hands of a person in one frame image of a video; dividing the area into multiple blocks and calculating a motion vector for each of the blocks; clustering multiple resulted motion vectors into a first cluster and a second cluster, wherein multiple first blocks corresponding to the first cluster of motion vectors correspond to one of a left hand and a right hand, and multiple second blocks corresponding to the second cluster of motion vectors correspond to the other one of the left hand and the right hand; identifying movements of the hands to which the first cluster and the second cluster correspond in a frame image subsequent to the one frame image; and matching the identified movements with a predetermined action mode to determine an action of the hands.