Abstract: A method described herein includes receiving a digital image, wherein the digital image includes a first element that corresponds to a first domain and a second element that corresponds to a second domain. The method also includes automatically assigning a label to the first element in the digital image based at least in part upon a computed probability that the label corresponds to the first element, wherein the probability is computed through utilization of a first model that is configured to infer labels for elements in the first domain and a second model that is configured to infer labels for elements in the second domain. The first model receives data that identifies learned relationships between elements in the first domain and elements in the second domain, and the probability is computed by the first model based at least in part upon the learned relationships.

Abstract: The present disclosure relates to a network training method, an electronic device and a storage medium. The network training method includes the following steps. At least one implicit vector may be input into at least one pre-trained generative network to obtain a first generated image; the generative network may be obtained with a discriminative network through adversarial trainings with a plurality of natural images. A degradation process may be performed on the first generated image to obtain a first degraded image of the first generated image. The implicit vector and the generative network may be trained according to the first degraded image and a second degraded image of at least one target image; the trained generative network and the trained implicit vector may be used to generate at least one reconstructed image of the target image.

Abstract: Object recognition methods and devices, and storage media are provided. In one aspect, an object recognition method includes: acquiring to-be-processed point cloud data and processing the to-be-processed point cloud data to obtain target point cloud data, the to-be-processed point cloud data including point cloud data of an to-be-recognized object; recognizing the to-be-recognized object from the target point cloud data and determining a target feature of the to-be-recognized object; and determining, according to the target feature, a target category to which the to-be-recognized object belongs among a plurality of categories to obtain a recognition result for the to-be-recognized object. The recognition result includes at least the target category.

Abstract: Methods and apparatuses for deep learning-based recommendation, electronic devices, and media include: respectively obtaining related information of a target user and related information of a target item; respectively using at least two sub-models in an integrated model to obtain, based on the related information of the target user and the related information of the target item, operating probabilities corresponding to the at least two sub-models; obtaining, based on the operating probabilities corresponding to the at least two sub-models, a target probability about the target user operating the target item; and recommending the target item to the target user based on the target probability.

Abstract: Input sound spectra are acquired. The input sound spectra include sound spectra corresponding to multiple sound sources. Predicted sound spectra are isolated from the input sound spectra by performing spectrum isolation processing on the input sound spectra. Updated input sound spectra are acquired by removing the predicted sound spectra from the input sound spectra. Next isolated predicted sound spectra continue to be acquired through the updated input sound spectra, until the updated input sound spectra include no sound spectrum.

Abstract: An anchor determination includes: performing feature extraction on an image to be processed to obtain a first feature map of the image to be processed; performing anchor prediction on the first feature map via an anchor prediction network to obtain position information of anchors and shape information of the anchors in the first feature map, the position information of the anchors referring to information about positions in the first feature map where the anchors are generated. A corresponding anchor determination apparatus and a storage medium are also provided.

Abstract: A key frame scheduling method and apparatus include: performing feature extraction on a current frame through a first network layer of a neural network to obtain low-layer features of the current frame acquiring a scheduling probability of the current frame according to low-level features of a previous key frame adjacent to the current frame and the low-level features of the current frame; determining whether the current frame is scheduled as a key frame according to the scheduling probability value of the current frame; and when determining that the current frame is scheduled as a key frame, performing feature extraction on low-level features of a current key frame via a second network layer of the neural network to obtain high-level features of the current key frame, where the network depth of the first network layer is less than the network depth of the second network layer.

Abstract: A search method, a search device, a storage medium and a computer program. The search method includes: determining a first similarity between text and at least one video, the text being used for representing a search condition; determining a first character interaction graph of the text and a second character interaction graph of the at least one video; determining a second similarity between the first character interaction graph and the second character interaction graph; and according to the first similarity and the second similarity, determining a video matching the search condition from the at least one video.

Abstract: The present disclosure relates to a method and device for video processing, an electronic device, and a storage medium. The method comprises: determining, on the basis of paragraph information of a query text paragraph and video information of multiple videos in a video library, preselected videos associated with the query text paragraph in the multiple videos; and determining a target video in the preselected videos on the basis of video frame information of the preselected videos and of sentence information of the query text paragraph. The method for video processing of the embodiments of the present disclosure indexes videos by means of the relevance between the videos and the query text paragraph, allows the pinpointing of the target video, avoids search result redundancy, allows the processing of the query text paragraph in a natural language form, and is not limited by the inherent contents of content labels.

Abstract: An image processing method and a device, and a network training method and a device are provided. The image processing method includes determining a guide group arranged on an image to be processed and directed at a target object, the guide group comprising at least one guide point, and the guide point being used to indicate the position of a sampling pixel, and the magnitude and direction of the motion speed of the sampling pixel; and on the basis of the guide point in the guide group and the image to be processed, performing optical flow prediction to obtain the motion of the target object in the image to be processed.

Abstract: Provided are a method and apparatus for neutral network training and a method and apparatus for image generation. The method includes that: a first random vector is input to a generator to obtain a first generated image; the first generated image and a first real image are input to a discriminator to obtain a first discriminated distribution and a second discriminated distribution; a first network loss of the discriminator is determined based on the first discriminated distribution, the second discriminated distribution, a first target distribution and a second target distribution; a second network loss of the generator is determined based on the first discriminated distribution and the second discriminated distribution; and adversarial training is performed on the generator and the discriminator based on the first network loss and the second network loss.

Abstract: The present disclosure relates an image processing method and device, and a storage medium. The method comprises: performing a feature equalization processing on a sample image by an equalization subnetwork of a detection network to obtain an equalized feature image of the sample image; performing a target detection processing on the equalized feature image by a detection subnetwork of the detection network to obtain predicted regions of a target object in the equalized feature image; determining an intersection-over-union of each of the predicted regions respectively; sampling the plurality of predicted regions according to the intersection-over-union of each predicted region to obtain a target region; and training the detection network according to the target region and a labeled region. The systems and techniques disclosed here can reduce information loss and improve the training effect and training efficiency.

Abstract: An example of the present disclosure provides methods, apparatuses and devices for magnifying a feature map, and a computer readable storage medium. The method includes: receiving a source feature map to be magnified; obtaining N reassembly kernels corresponding to each source position in the source feature map by performing convolution on the source feature map, wherein N refers to a square of a magnification factor of the source feature map; obtaining, for each of the reassembly kernels, a normalized reassembly kernel by performing normalization; obtaining, for each source position in the source feature map, N reassembly features corresponding to the source position by reassembling features of a reassembly region determined according to the source position with N normalized reassembly kernels corresponding to the source position; and generating a target feature map according to the N reassembly features corresponding to each source position in the source feature map.

Abstract: An example of the present disclosure provides methods, apparatuses and devices for magnifying a feature map, and a computer readable storage medium. The method includes: receiving a source feature map to be magnified; obtaining N reassembly kernels corresponding to each source position in the source feature map by performing convolution on the source feature map, wherein N refers to a square of a magnification factor of the source feature map; obtaining, for each of the reassembly kernels, a normalized reassembly kernel by performing normalization; obtaining, for each source position in the source feature map, N reassembly features corresponding to the source position by reassembling features of a reassembly region determined according to the source position with N normalized reassembly kernels corresponding to the source position; and generating a target feature map according to the N reassembly features corresponding to each source position in the source feature map.

Abstract: Time domain action detecting methods and systems, electronic devices, and computer storage medium are provided. The method includes: obtaining a time domain interval in a video with an action instance and at least one adjacent segment in the time domain interval; separately extracting action features of at least two video segments in candidate segments, where the candidate segments comprises video segment corresponding to the time domain interval and adjacent segments thereof; pooling the action features of the at least two video segments in the candidate segments, to obtain a global feature of the video segment corresponding to the time domain interval; and determining, based on the global feature, an action integrity score of the video segment corresponding to the time domain interval. The embodiments of the present disclosure benefit accurately determining whether a time domain interval comprises an integral action instance, and improve the accuracy rate of action integrity identification.

Abstract: A key frame scheduling method and apparatus include: performing feature extraction on a current frame through a first network layer of a neural network to obtain low-layer features of the current frame acquiring a scheduling probability of the current frame according to low-level features of a previous key frame adjacent to the current frame and the low-level features of the current frame; determining whether the current frame is scheduled as a key frame according to the scheduling probability value of the current frame; and when determining that the current frame is scheduled as a key frame, performing feature extraction on low-level features of a current key frame via a second network layer of the neural network to obtain high-level features of the current key frame, where the network depth of the first network layer is less than the network depth of the second network layer.

Abstract: The present disclosure relates to a method and device for video processing, an electronic device, and a storage medium. The method comprises: determining, on the basis of paragraph information of a query text paragraph and video information of multiple videos in a video library, preselected videos associated with the query text paragraph in the multiple videos; and determining a target video in the preselected videos on the basis of video frame information of the preselected videos and of sentence information of the query text paragraph. The method for video processing of the embodiments of the present disclosure indexes videos by means of the relevance between the videos and the query text paragraph, allows the pinpointing of the target video, avoids search result redundancy, allows the processing of the query text paragraph in a natural language form, and is not limited by the inherent contents of content labels.

Abstract: An anchor determination includes: performing feature extraction on an image to be processed to obtain a first feature map of the image to be processed; performing anchor prediction on the first feature map via an anchor prediction network to obtain position information of anchors and shape information of the anchors in the first feature map, the position information of the anchors referring to information about positions in the first feature map where the anchors are generated. A corresponding anchor determination apparatus and a storage medium are also provided.

Abstract: A method described herein includes receiving a digital image, wherein the digital image includes a first element that corresponds to a first domain and a second element that corresponds to a second domain. The method also includes automatically assigning a label to the first element in the digital image based at least in part upon a computed probability that the label corresponds to the first element, wherein the probability is computed through utilization of a first model that is configured to infer labels for elements in the first domain and a second model that is configured to infer labels for elements in the second domain. The first model receives data that identifies learned relationships between elements in the first domain and elements in the second domain, and the probability is computed by the first model based at least in part upon the learned relationships.

Abstract: Methods and apparatuses for deep learning-based recommendation, electronic devices, and media include: respectively obtaining related information of a target user and related information of a target item; respectively using at least two sub-models in an integrated model to obtain, based on the related information of the target user and the related information of the target item, operating probabilities corresponding to the at least two sub-models; obtaining, based on the operating probabilities corresponding to the at least two sub-models, a target probability about the target user operating the target item; and recommending the target item to the target user based on the target probability.