Abstract: Disclosed are techniques for augmenting video datasets for training machine learning algorithms with additional video datasets that are cropped copies of the video datasets. Frames of a received video dataset are divided into a plurality of subframes. For each subframe, a count is tallied corresponding to the cumulative number of pixels changed across the frames of the received video. Counts are compared to determine which subframe includes the most changed pixels across the frames of the video dataset, which is selected as a cropping candidate. The cropping candidate is used to generate copies of the video dataset that are cropped to include at least the cropping candidate and exclude at least some of the remaining portions of each frame of the video dataset that are outside of the cropping candidate. In some embodiments, boundaries of cropping candidates are transformed to generate a plurality of cropped variations of the video dataset.

Abstract: A method for training a generator. The generator is supplied with at least one actual signal that includes real or simulated physical measured data from at least one observation of the first area. The actual signal is translated by the generator into a transformed signal that represents the associated synthetic measured data in a second area. Using a cost function, an assessment is made concerning to what extent the transformed signal is consistent with one or multiple setpoint signals, at least one setpoint signal being formed from real or simulated measured data of the second physical observation modality for the situation represented by the actual signal. Trainable parameters that characterize the behavior of the generator are optimized with the objective of obtaining transformed signals that are better assessed by the cost function. A method for operating the generator, and that encompasses the complete process chain are also provided.

Abstract: Systems and methods for training a conditional generator model are described. Methods receive a sample, and determine a discriminator loss for the received sample. The discriminator loss is based on an ability to determine whether the sample is generated by the conditional generator model or is a ground truth sample. The method determines a secondary loss for the generated sample and updates the conditional generator model based on an aggregate of the discriminator loss and the secondary loss.

Abstract: Techniques are disclosed for using and training a descriptor network. An image may be received and provided to the descriptor network. The descriptor network may generate an image descriptor based on the image. The image descriptor may include a set of elements distributed between a major vector comprising a first subset of the set of elements and a minor vector comprising a second subset of the set of elements. The second subset of the set of elements may include more elements than the first subset of the set of elements. A hierarchical normalization may be imposed onto the image descriptor by normalizing the major vector to a major normalization amount and normalizing the minor vector to a minor normalization amount. The minor normalization amount may be less than the major normalization amount.

Abstract: Systems described herein may use machine classifiers to perform a variety of natural language understanding tasks including, but not limited to multi-turn dialogue generation. Machine classifiers in accordance with aspects of the disclosure may model multi-turn dialogue as a one-to-many prediction task. The machine classifier may be trained using adversarial bootstrapping between a generator and a discriminator with multi-turn capabilities. The machine classifiers may be trained in both auto-regressive and traditional teacher-forcing modes, with the maximum likelihood loss of the auto-regressive outputs being weighted by the score from a metric-based discriminator model. The discriminators input may include a mixture of ground truth labels, the teacher-forcing outputs of the generator, and/or negative examples from the dataset. This mixture of input may allow for richer feedback on the autoregressive outputs of the generator.

Abstract: A training system for training a generator neural network arranged to transform measured sensor data into generated sensor data. The generator network is arranged to receive as input sensor data and a transformation goal selected from a plurality of transformation goals and is arranged to transform the sensor data according to the transformation goal.

Abstract: This document describes change detection criteria for updating sensor-based maps. Based on an indication that a registered object is detected near a vehicle, a processor determines differences between features of the registered object and features of a sensor-based reference map. A machine-learned model is trained using self-supervised learning to identify change detections from inputs. This model is executed to determine whether the differences satisfy change detection criteria for updating the sensor-based reference map. If the change detection criteria is satisfied, the processor causes the sensor-based reference map to be updated to reduce the differences, which enables the vehicle to safely operate in an autonomous mode using the updated reference map for navigating the vehicle in proximity to the coordinate location of the registered object.

Abstract: The present disclosure discloses a method and device for optimizing an object-class model based on a neural network. The method includes: establishing the object-class model based on the neural network, training the object-class model, and realizing classification of target images by using the object-class model that has been trained; and when a new target image is generated, and the new target image is an image corresponding to a new condition of a target and is capable of still being classified into an original classification system, judging a result of identification of the object-class model to the new target image, and if the object-class model is not capable of correctly classifying the new target image, according to the new target image, selecting some of parameters, adjusting the some of parameters, and training to obtain an object-class model that is capable of correctly classifying the new target image.

Abstract: A method for controlling a robotic device is presented. The method includes capturing an image corresponding to a current view of the robotic device. The method also includes identifying a keyframe image comprising a first set of pixels matching a second set of pixels of the image. The method further includes performing, by the robotic device, a task corresponding to the keyframe image.

Abstract: A laser system for amplifying laser light generated from a laser light source and emitting the laser light includes an optical element in an optical path of the laser light and transmits the laser light, a control device to control power to be supplied to the laser system, an imager to capture an image of the optical element, and an image processing circuitry to process the image of the optical element captured by the imager. The image processing circuitry in which reference images of the optical element corresponding to power information relating to the power are prepared in advance includes a comparison unit to compare a captured image of the optical element captured by the imager with a reference image selected by a reference image selection unit, the reference image corresponding to the power information at a time of image capturing by the imager.

Abstract: The image processing apparatus for performing display restriction processing on a captured image captured by a moving robot includes: a task acquisition unit configured to acquire information that corresponds to a property of a task to be executed via the remote operation performed on the moving robot; a target object identification unit configured to identify target objects in the captured image; a restricted target object specification unit configured to specify a target object for which a display restriction is required among the target objects identified in the target object identification unit in accordance with the property of the task to be executed by the moving robot based on the above information; and a display restriction processing unit configured to perform the display restriction processing on a restricted area in the captured image that corresponds to the target object for which display restriction is required.

Abstract: One embodiment of the present invention sets forth a technique for generating simulated training data for a physical process. The technique includes receiving, as input to at least one machine learning model, a first simulated image of a first object, wherein the at least one machine learning model includes mappings between simulated images generated from models of physical objects and real-world images of the physical objects. The technique also includes performing, by the at least one machine learning model, one or more operations on the first simulated image to generate a first augmented image of the first object. The technique further includes transmitting the first augmented image to a training pipeline for an additional machine learning model that controls a behavior of the physical process.

Abstract: The subject matter described herein includes methods, systems, and computer readable media for mask embedding for realistic high-resolution image synthesis. According to one method for mask embedding for realistic high-resolution image synthesis includes receiving, as input, a mask embedding vector and a latent features vector, wherein the mask embedding vector acts as a semantic constraint; generating, using a trained image synthesis algorithm and the input, a realistic image, wherein the realistic image is constrained by the mask embedding vector; and outputting, by the trained image synthesis algorithm, the realistic image to a display or a storage device.

Abstract: A system and method for providing unsupervised domain adaption for spatio-temporal action localization that includes receiving video data associated with a source domain and a target domain that are associated with a surrounding environment of a vehicle. The system and method also include analyzing the video data associated with the source domain and the target domain and determining a key frame of the source domain and a key frame of the target domain. The system and method additionally include completing an action localization model to model a temporal context of actions occurring within the key frame of the source domain and the key frame of the target domain and completing an action adaption model to localize individuals and their actions and to classify the actions based on the video data. The system and method further include combining losses to complete spatio-temporal action localization of individuals and actions.

Abstract: A method of image processing based on a plurality of frames of images, an electronic device, and a storage medium are provided. The method includes: capturing a plurality of frames of original images; obtaining a high dynamic range (HDR) image by performing image synthesis on the plurality of frames of original images; performing artificial intelligent-based denoising on the HDR image to obtain a target denoised image.

Abstract: A system for categorizing seeds of plants into hybrid and non-hybrid categories. Seeds sorted according to the disclosed system are also disclosed.

Abstract: An information processing method for embedding watermark bits into weights of a first neural network includes: obtaining an output of a second neural network by inputting a plurality of input values obtained from a plurality of weights of the first neural network to the second neural network; obtaining second gradients of the respective plurality of input values based on an error between the output of the second neural network and the watermark bits; and updating the weights based on values obtained by adding first gradients of the weights of the first neural network that have been obtained based on backpropagation and the respective second gradients.

Abstract: A computer system uses Learning from Demonstration (LfD) techniques in which a multitude of tasks are demonstrated without requiring careful task set up, labeling, and engineering, and learns multiple modes of behavior from visual data, rather than averaging the multiple modes. As a result, the computer system may be used to control a robot or other system to exhibit the multiple modes of behavior in appropriate circumstances.

Abstract: Methods and apparatus are disclosed for implementing machine learning data augmentation within the die of a non-volatile memory (NVM) apparatus using on-chip circuit components formed on or within the die. Some particular aspects relate to configuring under-the-array or next-to-the-array components of the die to generate augmented versions of images for use in training a Deep Learning Accelerator of an image recognition system by rotating, translating, skewing, cropping, etc., a set of initial training images obtained from a host device. Other aspects relate to configuring under-the-array or next-to-the-array components of the die to generate noise-augmented images by, for example, storing and then reading training images from worn regions of a NAND array to inject noise into the images.

Abstract: Action recognition methods are disclosed.

Abstract: A system for training a classification model to be robust against perturbations of multiple perturbation types. A perturbation type defines a set of allowed perturbations. The classification model is trained by, in an outer iteration, selecting a set of training instances of a training dataset; selecting, among perturbations allowed by the multiple perturbation types, one or more perturbations for perturbing the selected training instances to maximize a loss function; and updating the set of parameters of the classification model to decrease the loss for the perturbed instances. A perturbation is determined by, in an inner iteration, determining updated perturbations allowed by respective perturbation types of the multiple perturbation types and selecting an updated perturbation that most increases the loss of the classification model.

Abstract: A model testing system administers tests to machine learning (ML) models to test the accuracy and the robustness of the ML models. A user interface (UI) associated with the model testing system receives selections of one or more of a plurality of tests to be administered to a ML model under test. Test data produced by one or more of a plurality of testing ML models that correspond to the plurality of tests is provided to the ML model under test based on the selected tests. One or more of a generative patches test, a generative perturbations test and a counterfeit data test can be administered to the ML model under test based on the selections.

Abstract: A system, a method, and a computer program product for determining a sign type of a road sign are disclosed herein. The system comprises a memory configured to store computer-executable instructions and one or more processors configured to execute the instructions to obtain sensor data associated with the road sign, wherein the sensor data comprises data associated with counts of road sign observations, determine one or more features associated with the road sign, based on the obtained sensor data, and determine the sign type of the road sign, based on the one or more features.

Abstract: A method for generating a synthetic image with predefined properties. The method includes the steps of providing first values which characterize the predefined properties of the image that is to be generated and attention weights which characterize a weighting of one of the first values and feeding sequentially the first values and assigned attention weights as input value pairs into an generative automated learning system that includes at least a recurrent connection. An image generation system and a computer program that are configured to carry out the method are also described.

Abstract: An information processing method is executed by a computer, and includes: obtaining a first image generated by a multi-pinhole camera; extracting at least one point spread function (PSF) in each of a plurality of regions in the first image; obtaining a second image different from the first image, and reference data used in machine learning for the second image; generating a third image, by convolving each of a plurality of regions in the second image with at least one PSF extracted in a corresponding region of the plurality of regions in the first image; and outputting a pair of the reference data and the third image.

Abstract: A control method includes: deriving an approach location at which the end effector grips an operation object; deriving a scan location for scanning an identifier of the operation object; and based on the approach location and the scan location, creating or deriving a control sequence to instruct the robot to execute the control sequence. The control sequence includes (1) gripping the operation object from a start location; (2) scanning an identifier of the operation object with a scanner located between the start location and a task location; (3) temporarily releasing the operation object from the end effector and regripping the operation object by the end effector to be shifted, at a shift location, when a predetermined condition is satisfied; and (4) moving the operation object to the task location.

Abstract: The technology relates to assisting large self-driving vehicles, such as cargo vehicles, as they maneuver towards and/or park at a destination facility. This may include a given vehicle transitioning between different autonomous driving modes. Such a vehicles may be permitted to drive in a fully autonomous mode on certain roadways for the majority of a trip, but may need to change to a partially autonomous mode on other roadways or when entering or leaving a destination facility such as a warehouse, depot or service center. Large vehicles such as cargo truck may have limited room to maneuver in and park at the destination, which may also prevent operation in a fully autonomous mode. Here, information from the destination facility and/or a remote assistance service can be employed to aid in real-time semi-autonomous maneuvering.

Abstract: A system including: at least one processor; and at least one memory having stored thereon computer program code that, when executed by the at least one processor, controls the system to: receive a data model identification and a dataset; in response to determining that the data model does not contain a hierarchical structure, perform expectation propagation on the dataset to approximate the data model with a hierarchical structure; divide the dataset into a plurality of channels; for each of the plurality of channels: divide the data into a plurality of microbatches; process each microbatch of the plurality of microbatches through parallel iterators; and process the output of the parallel iterators through single-instruction multiple-data (SIMD) layers; and asynchronously merge results of the SIMD layers.

Abstract: An image generation means 81 generates an image using a generator. A discrimination means 82 discriminates whether an object image includes a feature of a target image, using a discriminator. A first update means 83 updates the generator so as to minimize a first error representing a degree of divergence between a result of discriminating a generated image using the discriminator and a correct answer label associated with the generated image, the generated image being the image generated using the generator. A second update means 84 updates the discriminator so as to minimize a second error representing a degree of divergence between each of respective results of discriminating the generated image, a first actual image including the feature of the target image, and a second actual image not including the feature of the target image using the discriminator and a correct answer label associated with a corresponding image.

Abstract: Technologies for image processing based on a creation workflow for creating a type of images are provided. Both multi-stage image generation as well as multi-stage image editing of an existing image are supported. To accomplish this, one system models the sequential creation stages of the creation workflow. In the backward direction, inference networks can backward transform an image into various intermediate stages. In the forward direction, generation networks can forward transform an earlier-stage image into a later-stage image based on stage-specific operations. Advantageously, this technical solution overcomes the limitations of the single-stage generation strategy with a multi-stage framework to model different types of variation at various creation stages. Resultantly, both novices and seasoned artists can use these technologies to efficiently perform complex artwork creation or editing tasks.

Abstract: An analysis method executed by a computer includes acquiring a refine image that maximizes a score for inferring a correct label by an inferring process using a trained model, the refine image being generated from an input image used when an incorrect label is inferred; generating a map indicating a region of pixels having the same or similar level of attention degree related to inference in the inferring process, of a plurality of pixels in the generated refine image, based on a feature amount used in the inferring process; extracting an image corresponding to a pixel region whose level in the generated map is a predetermined level, from calculated images calculated based on the input image and the refine image; and generating an output image that specifies a portion related to an inference error in the inferring process, among the calculated images, based on image processing on the extracted image.

Abstract: Disclosed are an image classification and conversion method, apparatus, image processor and training method thereof, and medium. The image classification method includes receiving a first input image and a second input image; performing image encoding on the first input image by utilizing n stages of encoding units connected in cascades to produce a first output image, wherein n is an integer greater than 1, and wherein as for 1?i<n, the output of the i-th stage of encoding unit is an input of an (i+1)-th stage of encoding unit, wherein m is an integer greater than 1; outputting a first output image, the first output image comprising mn output sub-images, and each of the mn output sub-images is corresponding to an image category.

Abstract: A date extractor disclosed herein allows extracting dates and date ranges from documents. An implementation of the date extractor is implemented using various computer process instructions including scanning a document to generate a plurality of tokens, assigning labels to token using named entity recognition machine to generate a named entity vector, extracting dates from the named entity vector by comparing each of the named entities of the named entity vector to predetermined patterns of dates to generate a date vector, generating a plurality of date pairs from the date vector, and extracting date-ranges by comparing the plurality of date pairs to predetermined patterns of date ranges.

Abstract: A technique making use of a few-shot model to determine graphical features present in an image based on a small set of examples with known graphical features. Where a support set including a number of images that each have a known combination of graphical features, the image recognition can identify unknown combinations of those graphical features in any number of query images. In an embodiment of the present disclosure examples of a filled-out form are used to interpret any number of additional filled out versions of the form.

Abstract: An information processing device and method for sharing of compressed training data for neural network training is provided. The information processing device receives a first image which includes an object of interest. The information processing device extracts, from the received first image, a region of interest which includes the object of interest. Once extracted, the extracted region of interest is provided to an input layer of N numbers of layers of a first neural network, trained on an object detection task. The information processing device selects an intermediate layer of the first neural network and extracts a first intermediate result as an output generated by the selected intermediate layer of the first neural network based on the input RoI. Once extracted, the information processing device shares the extracted first intermediate result as compressed training data with a server to train a second neural network on the object detection task.

Abstract: Method of detecting objects in non-perspective images starts by generating an arrangement of tiles based on a field of view of a non-perspective camera lens, a predetermined size of the tiles, and a predetermined maximum object radius. The arrangement of the tiles includes the minimum number of tiles to cover the field of view. A non-perspective image is then captured using the non-perspective camera lens. The non-perspective image may be a still image frame or a video. Using the tiles, a plurality of images are generated, respectively, and at least a portion of a first object is detected in one or more images. The first object is generated using the one or more images that include the at least the portion of the first object, and the first object is displayed on a display interface. Other embodiments are described herein.

Abstract: A method for controlling a robotic device is presented. The method includes capturing an image corresponding to a current view of the robotic device. The method also includes identifying a keyframe image comprising a first set of pixels matching a second set of pixels of the image. The method further includes performing, by the robotic device, a task corresponding to the keyframe image.

Abstract: A method and apparatus for recognizing an image, and a storage medium are provided. The method includes: obtaining labeling results of training data to be labeled in a target training data set; determining a weight coefficient of each labeling result in response to that the training data includes a plurality of labeling results; determining a label of the training data based on labeling result and the weight coefficient corresponding to the labeling result, wherein the training data with the label is the target training data for training a recognition model; and recognizing the image based on the recognition model trained based on the target training data.

Abstract: A apparatus for classifying a seed as inbred or hybrid comprises a terahertz signal source for emitting a terahertz signal towards the seed, a detector for detecting at least part of the terahertz signal having interacted with the seed, a scanner for moving the support relative to the terahertz signal to provide a scan of the seed, a data processing device for forming an image data from the detected terahertz signal as obtained for a plurality of positions during the scan of the seed, and a decision support system for providing a classification from the image data. In an embodiment, the terahertz signal source is arranged for emitting a continuous or pulse wave signal, and wherein the detector is arranged for detecting an amplitude and a phase of the terahertz signal having interacted with the seed. A signal representing an outcome of the classification may control a separator to separate seeds according to their classification.

Abstract: A scent visualization system may comprise a display apparatus for generating a target image including a target object; an olfactory sensor for detecting a scent; and a scent visualization apparatus for generating target associative visualization information that reminds the scent from the target image received from the display apparatus and sensing information received from the olfactory sensor, and generating an associative image by combining the target associative visualization information and the target image. Therefore, a low-cost, high-efficiency, high-utilization, and high-convenience scent visualization system can be provided.

Abstract: A computer-implemented detection method includes, in response to inputting a first image including a region of one or more objects to a learned model, identifying a first entire image corresponding to entirety of a first object as a detection candidate, the learned model being generated by learning training data including an image corresponding to a part of an object and an entire image corresponding to entirety of the object, detecting an existing region of the first target object in the first image in accordance with a comparison between the identified first entire image and the region of the one or more target objects, and determining, based on a specific image obtained by invalidating the existing region in the first image, whether another target object is included in the first image.

Abstract: A method for rendering a non-photorealistic (NPR) content from a set (SI) of at least one image of a same scene is provided. The set of images (SI) is associated with a depth image comprising a set of regions. Each region corresponds to a region of a given depth. The method for rendering a non-photorealistic content includes generation of a segmented image having at least one segmented region generated with a given segmentation scale. The at least one segmented region corresponds to at least one region of the set of regions. A binary edge image is generated in which at least one binary edge region is generated with a given edge extraction scale, the at least one binary edge region corresponding to at least one region of the set of regions. The non-photorealistic content is rendered by combining the segmented image and the binary edge image.

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for facilitating data transformations, according to embodiments of the present invention. In one embodiment, a plurality of remote sources is searched to identify candidate transformation tools relevant for performing data transformations. The candidate transformation tools are analyzed to identify tool examples corresponding with each of the candidate transformation tools. For each of the candidate transformation tools, the tool examples are stored in association with the corresponding candidate transformation tool. Based on a comparison of tool examples with example values, a transformation tool is identified as relevant to facilitate transforming example input values to the desired form in which to transform data.

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for facilitating data transformations, according to embodiments of the present invention. In one embodiment, a set of example values is received. An index to identify a plurality of data transformation tools that are relevant to the set of example values is referenced, wherein each of the data transformation tools correspond with one or more tool examples. The data transformation tools are ranked based on an extent of similarity between the set of example values and the tool examples. For data transformation tools associated with the extent of similarity that exceeds a similarity threshold, a transformation program is generated that uses the data transformation tool and a supplemental transformation tool to transform the one or more example input values to the desired form in which to transform data.

Abstract: Methods for detecting anomalies in a geographic area include receiving, from an electronic computing device, expected relationship data indicating expected relationships between a plurality of entities within the geographic area; detecting the plurality of entities within the geographic area; generating observed relationship data indicating observed relationships between the plurality of entities; identifying the expected relationships between the plurality of entities based on the expected relationship data; determining that a given observed relationship between two entities of the plurality of entities is likely to represent an anomaly based on the expected relationship data; and providing an electronic notification to a safety officer, the electronic notification indicating that the given observed relationship is likely to represent the anomaly.

Abstract: An image processing apparatus configured to divide an image into frequency bands and reduce noise, in which a first image includes a high-band frequency component, and a second image includes a low-band frequency component, includes: a first detecting unit configured to detect an edge in at least one of the first image and the second image; a second detecting unit configured to detect a low-contrast edge that is of a lower contrast than a contrast of the edge detected by the first detecting unit in the at least one of the first image and the second image; a compositing unit configured to composite the first image and the second image using a weighting corresponding to the edge and the low-contrast edge.

Abstract: A system and method for determining an optimal solution to an optimization problem in a swarm of candidate solutions is provided. The invention comprises generating a population of random particles, where each particle is representative of a candidate solution. Further, a best particle is identified from the generated population of particles. The best particle is representative of an optimal solution. The population of particles is categorised into similar and non-similar particle groups by applying one or more multivariate measurement techniques, and similarity between the particles of the non-similar particle group with best particle is updated by applying an imitation technique. The generated population is updated with updated particles and a new best particle is evaluated from said population. Furthermore, final best particle is determined by further updating the population of particles until one or more target conditions are achieved.

Abstract: A computer transforms high-dimensional data into low-dimensional data. (A) A distance matrix is computed from observation vectors. (B) A kernel matrix is computed from the distance matrix using a bandwidth value. (C) The kernel matrix is decomposed using an eigen decomposition to define eigenvalues. (D) A predefined number of largest eigenvalues are selected from the eigenvalues. (E) The selected largest eigenvalues are summed. (F) A next bandwidth value is computed based on the summed eigenvalues. (A) through (F) are repeated with the next bandwidth value until a stop criterion is satisfied. Each observation vector of the observation vectors is transformed into a second space using a kernel principal component analysis with the next bandwidth value and the kernel matrix. The second space has a dimension defined by the predefined number of first eigenvalues. Each transformed observation vector is output.

Abstract: The present disclosure relates to training a recommendation model to generate trait recommendations using one permutation hashing and populated-value-slot-based densification. In particular, the disclosed systems can train the recommendation model by computing sketch vectors corresponding to traits using one permutation hashing. The disclosed systems can then fill in unpopulated value slots of the sketch vectors using populated-value-slot-based densification. The disclosed systems can combine the resulting densified sketches to generate the trained recommendation model. For example, in some embodiments, the disclosed systems can combine the sketches by generating a plurality of locality sensitive hashing tables based on the sketches. In some embodiments, the disclosed systems generate a count sketch matrix based on the sketches and generate trait embeddings based on the count sketch matrix using spectral embedding.

Abstract: Methods and systems for digital image segmentation and annotation, including: receiving a digital image depicting, in part, an object of interest from an input file; one or more of manually and automatically adding a polygon around the object of interest to generate a segmented digital image; one or more of manually and automatically appending a label to the polygon around the object of interest to generate a segmented and annotated digital image, wherein the label indicates one or more of an identity and a characteristic of the object of interest; and outputting information related to the segmented and annotated digital image to an output file. Optionally, the polygon is one of a bounding box and a 4-point polygon. Optionally, the object of interest is a parking spot.