Abstract: Systems and methods for processing image data representing a document to remove deformations contained in the document are disclosed. A system may include one or more memory devices storing instructions and one or more processors configured to execute the instructions. The instructions may instruct the system to provide, to a machine learning system, a training dataset representing a plurality of documents containing a plurality of training deformations. The instructions may also instruct the system to use the machine learning system to process image data representing a target document containing a target document deformation. The machine learning system may generate restored image data representing the target document with the target document deformation removed. The instructions may further instruct the system to provide the restored image data to at least one of a graphical user interface, an image storage device, or a computer vision system.

Abstract: A method and system for image-based region detection. Transformation matrices are computed by performing image registration between a target image and each of one or more reference images. Each transformation matrix is for transforming each of the reference images into a coordinate system of the target image. An optimal reference image is selected from among the reference images based on similarity measures between the target image and each reference image. The transformation matrix of the selected reference image is applied to a reference map associated with the reference image in order to generate a target map for the target image. The target map includes region labels indicating regions shown in the target image.

Abstract: The present disclosure is directed to extracting text from form-like documents. In particular, a computing system can obtain an image of a document that contains a plurality of portions of text. The computing system can extract one or more candidate text portions for each field type included in a target schema. The computing system can generate a respective input feature vector for each candidate for the field type. The computing system can generate a respective candidate embedding for the candidate text portion. The computing system can determine a respective score for each candidate text portion for the field type based at least in part on the respective candidate embedding for the candidate text portion. The computing system can assign one or more of the candidate text portions to the field type based on the respective scores.

Abstract: A microscopy system comprises a microscope which can record at least one microscope image and a computing device which comprises a trained image processing model set up to calculate an image processing result based on the at least one microscope image. A method for verifying a trained image processing model, which can be performed by the computing device, can include receiving a validation image and an associated target image; entering the validation image into the trained image processing model, which calculates an output image therefrom; entering image data based on at least the output image and the associated target image into a trained verification model which is trained to calculate an evaluation that indicates a quality that depends on the image data for entered image data; and calculating an evaluation by the trained verification model based on the entered image data.

Abstract: A system includes a processor and a hardware accelerator coupled to the processor. The hardware accelerator includes data analysis elements configured to analyze a data stream based on configuration data and to output a result, and an integrated circuit device that includes a DMA engine that writes input data to and read output data from the data analysis elements, one or more preprocessing cores that receive the input data from the DMA engine prior to the DMA engine writing the input data to the one or more data analysis elements and perform custom preprocessing functions on the input data, and one or more post-processing cores that receive the output data from the DMA engine after the output data is read from the data analysis elements but prior to the output data being output to the processor and perform custom post-processing functions on the output data.

Abstract: An embodiment of the present disclosure provides an artificial intelligence apparatus for generating training data including a memory configured to store an artificial intelligence model, an input interface including a microphone or a camera, and a processor configured to receive, via the input interface, input data, generate an inference result corresponding to the input data by using the artificial intelligence model, receive feedback corresponding to the inference result, determine suitability of the input data and the feedback for updating the artificial intelligence model, and generate training data based on the input data and the feedback if the input data and the feedback are determined as data suitable for updating of the artificial intelligence model.

Abstract: Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems and methods describe techniques that search videos and media content to determine the presence of unknown objects, generate novel detectors trained to identify the unknown objects, and apply the novel detectors to historical media content to identify previous appearances of the unknown objects.

Abstract: A multiple instance learning device for analyzing 3D images, comprises a memory in which a multiple instance learning model is stored and at least one processor electrically connected to the memory, wherein the multiple instance learning model comprises a convolution block configured to derive a feature map for each of 2D instances of a 3D image inputted to the multiple instance learning model, a spatial attention block configured to derive spatial attention maps of the instances from the feature maps derived from the convolution block, an instance attention block configured to receive a result of combining the feature maps and the spatial attention maps and derive an attention score for each instance, and derive an aggregated embedding for the 3D image by aggregating embeddings of the instances according to the attention scores.

Abstract: An information management apparatus comprises a communication unit configured to communicate with a plurality of external apparatuses having learning functions, and a control unit configured to control the communication with the plurality of external apparatuses performed by the communication unit. The control unit, if supervisory data generated when a predetermined external apparatus executes a learning function is received from the predetermined external apparatus via the communication unit, selects, from among the plurality of external apparatuses, an external apparatus, other than the predetermined external apparatus, with which the supervisory data is to be shared, and performs control so that the supervisory data is transmitted to the selected external apparatus.

Abstract: Improved multi-modal machine learning networks integrate computer vision systems with language models. In certain embodiments, a computer vision system analyzes at least one image to generate a computer vision output. The language model generates an output based, at least in part, on a consideration of the computer vision output. The outputs of the language model can be generated by jointly considering textual information learned by the language model and visual content extracted by the computer vision system, thereby significantly improving the accuracy, breadth, and comprehensiveness of the outputs.

Abstract: The invention relates to a control device (1) for a vehicle for determining the perceptual load of a visual and dynamic driving scene. The control device is configured to: ?receive a sensor output (101) of a sensor (3), the sensor (3) sensing the visual driving scene, ?extract a set of scene features (102) from the sensor output (101), the set of scene features (102) representing static and/or dynamic information of the visual driving scene, ?determine the perceptual load (104) of the set of extracted scene features (102) based on a predetermined load model (103), the load model (103) being predetermined based on reference video scenes each being labelled with a load value, ?map the perceptual load to the sensed driving and ?determine a spatial and temporal intensity distribution of the perceptual load across the sensed driving scene. The invention further relates to a vehicle, a system and a method.

Abstract: A process and a system for creating a visual guide for developing training data for a classification of image, where the training data includes images tagged with labels for the classification of the images. A processor may prompt a user to define a framework for the classification. For an initial set of images within the training data, qualified human classifiers are prompted to locate the images within the framework and to tag the images with labels. The processor determines whether the tagged images have consistent labels, and, if so, the processor adds images to the training data. The processor may add the images by providing a visual guide, the visual guide including tagged images arranged according to their locations within the framework their labels, and prompting human classifiers to tag the additional images with labels for the classification, according to the visual guide.

Abstract: An example apparatus for mining multi-scale hard examples includes a convolutional neural network to receive a mini-batch of sample candidates and generate basic feature maps. The apparatus also includes a feature extractor and combiner to generate concatenated feature maps based on the basic feature maps and extract the concatenated feature maps for each of a plurality of received candidate boxes. The apparatus further includes a sample scorer and miner to score the candidate samples with multi-task loss scores and select candidate samples with multi-task loss scores exceeding a threshold score.

Abstract: A method for performing a crowdsourcing task is provided. The method executed on a computing device comprises generating a crowdsourcing task to take at least one image of an original face photo; sending the generated crowdsourcing task to a crowdsourcing server to publish it thereon, the crowdsourcing server communicating with at least one user device registered in the crowdsourcing server; receiving at least one confirmation from the crowdsourcing server, each confirmation corresponding to the crowdsourcing task accepted by a particular user with a corresponding one of the registered user devices; providing an original face photo for each user corresponding to one of the received confirmations; sending, through the crowdsourcing server, the original face photo to a particular user device corresponding to the user; and receiving at least one image from the user device, the received images being taken for the original face photo in accordance with the accepted crowdsourcing task.

Abstract: An image description information generation method includes obtaining a to-be-processed target image, and inputting the target image into a target-image description information generation network. The target-image description information generation network is a generation network that is obtained by performing adversarial training using a plurality of sample images and that is configured to generate image description information, the adversarial training is training an initialized image description information generation network and an initialized discriminative network alternately, and the discriminative network is configured to discriminate an output result of the image description information generation network. The method also includes according to the output result of the target-image description information generation network, generating target-image description information used for describing the target image.

Abstract: In various examples, object detections of a machine learning model are leveraged to automatically generate new ground truth data for images captured at different perspectives. The machine learning model may generate a prediction of a detected object at the different perspective, and an object tracking algorithm may be used to track the object through other images in a sequence of images where the machine learning model may not have detected the object. New ground truth data may be generated as a result of the object tracking algorithms outputs, and the new ground truth data may be used to retrain or update the machine learning model, train a different machine learning model, or increase the robustness of a ground truth data set that may be used for training machine learning models from various perspectives.

Abstract: In some implementations, a device may obtain a set of input images, of an object of interest, that includes images that have a plain background. The device may obtain a set of background images that includes images associated with the object of interest. The device may generate, for an input image, a first modified image of the input image that removes a plain background of the input image. The device may generate, for the input image, a second modified image of the input image that is based on the first modified image and a background image. The device may generate, for the input image, a training image that includes an indication of a location of the object of interest depicted in the training image. The device may provide the training image to a training set, that includes a set of training images, for a computer vision model.

Abstract: Methods and systems for performing concept-based adversarial generation with steerable and diverse semantics. One system includes an electronic processor configured to access an input image. The electronic processor is also configured to perform concept-based semantic image generation based on the input image. The electronic processor is also configured to perform concept-based semantic adversarial learning using a set of semantic latent spaces generated as part of performing the concept-based semantic image generation. The electronic processor is also configured to generate an adversarial image based on the concept-based semantic adversarial learning. The electronic processor is also configured to test a target model using the adversarial image.

Abstract: Embodiments are disclosed for generative image congealing which provides an unsupervised learning technique that learns transformations of real data to improve the image quality of GANs trained using that image data. In particular, in one or more embodiments, the disclosed systems and methods comprise generating, by a spatial transformer network, an aligned real image for a real image from an unaligned real dataset, providing, by the spatial transformer network, the aligned real image to an adversarial discrimination network to determine if the aligned real image resembles aligned synthetic images generated by a generator network, and training, by a training manager, the spatial transformer network to learn updated transformations based on the determination of the adversarial discrimination network.

Abstract: Various embodiments are described that relate to classification of an unknown object. A time series signal associated with an unknown object can be obtained from a sensor. The time series signal can be subjected to a transform set, such as a Fourier transform and a discrete cosine transform, to produce a transform outcome. Based, at least in part, on the transform outcome, the unknown object can be classified.

Abstract: Methods, systems, and apparatus for detecting correlated objects involved in images are provided. In one aspect, a method includes: detecting a face object, a preset body part object, and a hand object involved in an image, performing a respective correlation prediction on every two of the face object, the preset body part object, and the hand object to obtain first, second, and third correlation prediction results, segmenting the image to determine at least one body object involved in the image to determine a first body object to which the face object belongs and a second body object to which the preset body part object belongs, adjusting the first correlation prediction result based on the first body object and the second body object, and determining correlated objects involved in the image according to the adjusted first correlation prediction result and the second and third correlation prediction results.

Abstract: System and method of deploying a trained machine learning neural network (MLNN) in generating a fall injury condition of a subject. The method comprises receiving, at input layers of the trained MLNN, millimeter wave (mmWave) radar point cloud data representing fall attributes from monitoring the subject via mmWave radar sensing device, the input layers associated with the fall attributes, receiving, at a second set of input layers, personal attributes of the subject associated with ones of the second set of input layers, the first and second sets of input layers interconnected with an output layer of the trained MLNN via intermediate layers, the trained MLNN produced by establishing a correlation between an injury condition of prior subjects and mmWave point cloud data and personal attributes associated with the prior subjects, and generating, at the output layer, the fall injury condition attributable to the subject.

Abstract: Enclosed are embodiments for scoring one or more trajectories of a vehicle through a given traffic scenario using a machine learning model that predicts reasonableness scores for the trajectories. In an embodiment, human annotators, referred to as a “reasonable crowd,” are presented with renderings of two or more vehicle trajectories traversing through the same or different traffic scenarios. The annotators are asked to indicate their preference for one trajectory over the other(s). Inputs collected from the human annotators are used to train the machine learning model to predict reasonableness scores for one or more trajectories for a given traffic scenario. These predicted trajectories can be used to rank trajectories generated by a route planner based on their scores, compare AV software stacks, or used by any other application that could benefit from a machine learning model that scores vehicle trajectories.

Abstract: The present disclosure provides a data processing method, an apparatus, an electronic device and a medium, which relates to the technical fields of autonomous driving, electronic maps, deep learning, image processing, and the like. The method includes: a computing device inputs a reference image and a captured image into a feature extraction model; obtain, a set of reference descriptors based on the first descriptor map; determine a plurality of sets of training descriptors; obtain a predicted pose of the vehicle by inputting the plurality of training poses and a plurality of similarities into a pose prediction model; and train the feature extraction model and the pose prediction model. When applied to a vehicle localization system, the trained feature extraction model and pose prediction model according to some embodiments of the present disclosure can improve accuracy and robustness of vehicle localization, thereby boosting the performance of the vehicle localization system.

Abstract: Generating visual data by defining a first action into a first set of objects and corresponding first set of motions, and defining a second action into a second set of objects and corresponding second set of motions. A relationship is then determined for the second action to the first action in terms of relationships between corresponding constituent objects and motions. Objects and motions are detected from visual data of first action. Visual data is composed for the second action from the data by transforming the constituent objects and motions detected in first action based on the corresponding determined relationships.

Abstract: An apparatus for training an image classification model according to an embodiment disclosed includes a first trainer that trains a model body and a first head through supervised learning based on a labeled data set subjected to type 1 labeling, a second trainer that trains the model body, the first head, and a second head through multi-task learning based on the labeled data set and an unlabeled data set, and a third trainer that trains a plurality of third heads through supervised learning based on the labeled data set subjected to type 2 labeling while freezing the model body.

Abstract: A computer implemented method for algorithmically editing digital video content is disclosed. A video file containing source video is processed to extract metadata. Label taxonomies are applied to extracted metadata. The labelled metadata is processed to identify higher-level labels. Identified higher-level labels are stored as additional metadata associated with the video file. A clip generating algorithm applies the stored metadata for selectively editing the source video to generate a plurality of different candidate video clips. Responsive to determining a clip presentation trigger on a viewer device, a clip selection algorithm is implemented that applies engagement data and metadata for the candidate video clips to select one of the stored candidate video clips. The engagement data is representative of one or more engagement metrics recorded for at least one of the stored candidate video clips. The selected video clip is presented to one or more viewers via corresponding viewer devices.

Abstract: According to the present disclosure, a weight parameter of a neural network is divided into a plurality of portions having a certain size and approximation is individually performed on the portions using a weighted sum of the codebook vectors.

Abstract: Automatic object optimization to accelerate machine learning training is disclosed. A request for a machine learning training dataset comprising a plurality of objects is received from a requestor. The plurality of objects includes data for training a machine learning model. A uniqueness characteristic for objects of the plurality of objects is determined, the uniqueness characteristic being indicative of how unique each object is relative to each other object. A group of objects from the plurality of objects is sent to the requestor, the group of objects being selected based at least partially on the uniqueness characteristic or sent in an order based at least partially on the uniqueness characteristic.

Abstract: Systems and methods for analyzing image data to identify cabinet products are disclosed. A computer-implemented method may include receiving, from an electronic device via a network connection, at least one digital image depicting a cabinet. The method also may include analyzing, by one or more processors, the at least one digital image to determine a first set of characteristics of the cabinet. Additionally, the method may include accessing, by the one or more processors from memory, a second set of characteristics corresponding to a plurality of cabinet products and comparing the first set of characteristics to the second set of characteristics to identify a cabinet product of the plurality of cabinet products that matches the cabinet. Further, the method may include transmitting, to the electronic device via the network connection, an indication of the cabinet product.

Abstract: A computer-implemented method of training an autoencoder to recover missing data is provided. The autoencoder includes an encoder for encoding its inputs into a latent space and a decoder for decoding the encodings from the latent space. The method comprises creating a first training set including a valid data set of multiple dimensions, and training the encoder and the decoder in a first training stage using the first training set to reduce a difference between the valid data set provided to the encoder and a data set decoded by the decoder. The method further comprises creating a second training set comprising an invalid data set, and training the encoder in a second training stage using the second training set to reduce a difference between encodings of valid data instances and encodings of their corresponding invalid data instances.

Abstract: An information processing apparatus includes a hardware processor which (i) performs learning by a learning data set associated with a correct answer label for a preset problem and creates a machine learning model for estimating a correct answer to the preset problem for input data, (ii) estimates the correct answer to the preset problem for the input data by using the machine learning model, (iii) in response to a user operation, determines a label indicating a result of the estimation as a correct answer label of the input data or corrects the label to determine the corrected label as a correct answer label of the input data, and (iv) additionally registers the determined correct answer label as learning data in association with the input data in the learning data set.

Abstract: A system for auto classification of products includes an entity recognizer and a model selector. The entity recognizer receives training data including an attribute of a product. The model selector selects a feature from the training data using a first statistical model to provide a first feature and a second statistical model to provide a second feature, and trains a probabilistic classifier using the first and the second features for providing a first and a second classification models respectively. Further, the model selector calculates an accuracy score of the obtained classification models for each distinct category in a preset hierarchy of categories and selects a classification model from the obtained classification models based on the accuracy score. The selected classification model has a highest accuracy score for a corresponding category in the preset hierarchy.

Abstract: A method of generating log data that includes a plurality of records, in which information detected through image recognition processing on a plurality of image frames is recorded. The method includes, detecting objects in each image frame through the image recognition processing, generating a record including identification information of each image frame and the aggregated number of objects detected in the image frame for each image frame, and adding metadata relating to the detected objects to the corresponding record when the aggregated number of objects is equal to or greater than one. When the aggregated number of objects is zero, the metadata related to the detected objects is not added to the record.

Abstract: A method for identifying a scene, comprising a computing device receiving a plurality of data points corresponding to a scene; the computing device determining one or more subsets of data points from the plurality of data points that are indicative of at least one sub-scene in said scene, said at least one sub-scene displayed on a display device that is part of said scene, wherein said at least one sub-scene does not represent said scene; the computing device categorizing said scene, disregarding said at least one sub-scene, wherein the categorizing includes interpreting said scene by a computer vision system such that said at least one sub-scene is not taken into account in the categorizing of said scene.

Abstract: A method of selecting an artificial intelligence (AI) model based on input data to select an AI model capable of correctly obtaining a result corresponding to data to be classified, e.g., a classification result indicating one of at least one class, from among a plurality of AI models, and a display device for performing the method.

Abstract: A computer-implemented method, a computer program product, and a system for reducing labeled sample quantities required to update test sets. The computer-implemented method includes inputting a portion of unlabeled production data into a base model and generating labeled output relating to the unlabeled production data. The computer-implemented method also includes inputting the labeled output into a performance predictor. The performance predictor is a meta model of the base model that is trained with another portion of the unlabeled production data, a training set used to train the base model, and a test set portioned from the training set. The computer-implemented method further includes outputting, by the performance predictor, a performance metric relating to the labeled output produced by the trained base model. The performance metric can be any metric capable of measuring the output performance of the base model.

Abstract: A system and method includes generating approximate distributions for distinct classes of data samples; computing a first partial Jensen-Shannon (JS) divergence and a second partial JS divergence based on the approximate distribution of the disparity affected class of data samples with reference to the approximate distribution of the control class of data samples; computing a disparity divergence based on the first partial JS divergence and the second partial JS divergence; generating a distribution-matching term based on the disparity divergence, wherein the distribution-matching term mitigates an inferential disparity between the control class of data samples and the disparity affected class of data samples during a training of an unconstrained artificial neural network; constructing a disparity-constrained loss function based on augmenting a target loss function with the distribution-matching term; and transforming the unconstrained ANN to a disparity-constrained ANN based on a training of the unconstraine

Abstract: An image processing apparatus 10 includes an interface 12 configured to acquire an image and a processor 13 configured to perform low-rank approximation by singular value decomposition on the acquired image, and perform object recognition on the acquired image subjected to the low-rank approximation by the singular value decomposition.

Abstract: Systems and methods for automatically detecting, classifying, and processing objects captured in an images or videos are provided. In one embodiment, the system receives an image from an image source and detects one or more objects in the image. The system performs a high-level classification of the one or more objects in the image. The system performs a specific classification of the one or more objects, determines a price of the one or more objects, and generates a pricing report comprising a price of the one or more objects. In another embodiment, the system captures at least one image or video frame and classifies an object present in the image or video frame using a neural network. The system adds the classified object and an assigned object code to an inventory and processes the inventory to assign the classified object a price.

Abstract: A measuring system 1 includes a server 200 identifying a kind of a product from a product image in which the product is included and a measuring device 100 identifying the kind of the product from the target image in which the product is included. The server 200 includes an acquisition unit that acquires a product image and product information relating to a kind of a product, a dividing unit that acquires a plurality of divided imaged by dividing the product image into a plurality of areas, and a generation unit that generates an identifying model by performing machine learning on the basis of a plurality of divided images extracted by an extraction unit that extracts a plurality of divided images satisfying a predetermined condition relating to a shown amount of the product from among the plurality of divided images.

Abstract: A supervised training device and method for training a neural network, and a supervised classification method and device based on the neural network are provided. The training device includes a storage unit and a processor. The processor accesses and executes a sampling module, a labelling module and a training module in the storage unit. The sampling module samples a first image and a second image from a first dataset. The labelling module tags the first image in response to a first control instruction to generate a first tagged image, and generates label data according to the first tagged image and the second image. The training module trains the neural network according to the label data.

Abstract: A method and system learn functions to be associated with data fields of forms to be incorporated into an electronic document preparation system. The functions are essentially sets of operations required to calculate the data field. The method and system receive form data related to a data field that expects data values resulting from performing specific operations. The method and system utilize machine learning and training set data to generate, test, and evaluate candidate functions to determine acceptable functions.

Abstract: Provided are a learning data collection apparatus, a learning data collection method, and a program for collecting learning data to be used for efficient retraining.

Abstract: Provided are an image processing apparatus and an image processing method that process a far-infrared image. The image processing apparatus includes a region extraction section, a modal transformation section, and a superimposition section. The region extraction section extracts a region of interest within a visible-light image captured by a visible-light camera. The modal transformation section receives an image of the region of interest within an infrared image captured by an infrared camera observing the same subject as the visible-light camera, and transforms the received image to a modal image. The superimposition section generates a presentation image by superimposing the modal image on the region of interest within the visible-light image. The modal transformation section transforms a far-infrared image of the region of interest to a modal image including an information modal familiar to humans by using, for example, a database and a conditional probability distribution.

Abstract: Systems and methods for performing spatial line grouping on digital ink stokes. The system includes an electronic processor configured to access a set of hypothetical lines in an electronic document and determine a set of hypothetical line pairings. The electronic processor is also configured to determine, via a gradient boosting tree model, a merge confidence score for each hypothetical line pairing and compare a first merge confidence score with a merge threshold. The first merge confidence score is associated with a first hypothetical line and a first neighboring hypothetical line. The electronic processor is also configured to, in response to the first merge confidence score satisfying the merge threshold, merge the first hypothetical line and the first neighboring hypothetical line to form a first line grouping. The electronic processor is also configured to perform a digital ink stroke analysis on the electronic document based on the first line grouping.

Abstract: A measuring system 1 includes a server 200 identifying a kind of a product from a product image in which the product is included and a measuring device 100 identifying the kind of the product from the target image in which the product is included. The server 200 includes an acquisition unit that acquires a product image and product information relating to a kind of a product, a dividing unit that acquires a plurality of divided imaged by dividing the product image into a plurality of areas, and a generation unit that generates an identifying model by performing machine learning on the basis of a plurality of divided images extracted by an extraction unit that extracts a plurality of divided images satisfying a predetermined condition relating to a shown amount of the product from among the plurality of divided images.

Abstract: The current embodiments relate to a real-time automated classification system that uses machine learning system to recognize important moments in broadcast content based on log data and/or other data received from various classification systems. The real-time automated classification system may be trained to recognize correlations between the various log data to determine key moments in the broadcast content. The real-time automated logging system may determine and generate metadata that describe or give information about what is happening or appearing in the broadcast content. The real-time automated logging system may automatically generate control inputs, suggestions, recommendations, and/or edits relating to broadcast content based upon the metadata, during broadcasting of the broadcast content.

Abstract: In various embodiments, a computer-implemented method of training a neural network for relighting an image is described. A first training set that includes source images and a target illumination embedding is generated, the source images having respective illuminated subjects. A second training set that includes augmented images and the target illumination embedding is generated, where the augmented images corresponding to the source images. A first autoencoder is trained using the first training set to generate a first output set that includes estimated source illumination embeddings and first reconstructed images that correspond to the source images, the reconstructed images having respective subjects that are i) from the corresponding source image, and ii) illuminated based on the target illumination embedding.

Abstract: Provided are a method and an apparatus for performing scalable video decoding, wherein the method and the apparatus down-sample input video, determine the down-sampled input video as base layer video, generate prediction video for enhancement layer video by applying an up-scaling filter to the base layer video, and code the base layer video and the prediction video, wherein the up-scaling filter is a convolution filter of a deep neural network.