Abstract: Enhanced methods and systems for the semantic segmentation of images are described. A refined segmentation mask for a specified object visually depicted in a source image is generated based on a coarse and/or raw segmentation mask. The refined segmentation mask is generated via a refinement process applied to the coarse segmentation mask. The refinement process correct at least a portion of both type I and type II errors, as well as refine boundaries of the specified object, associated with the coarse segmentation mask. Thus, the refined segmentation mask provides a more accurate segmentation of the object than the coarse segmentation mask. A segmentation refinement model is employed to generate the refined segmentation mask based on the coarse segmentation mask. That is, the segmentation model is employed to refine the coarse segmentation mask to generate more accurate segmentations of the object. The refinement process is an iterative refinement process carried out via a trained neural network.

Abstract: An object tracking method and apparatus are provided. The object tracking method includes detecting a target object in a first-type input image that is based on light in a first wavelength band, tracking the target object in the first-type input image based on detection information of the target object, measuring a reliability of the first-type input image by comparing the first-type image to an image in a database, comparing the reliability of the first-type input image to a threshold, and tracking the target object in a second-type input image that is based on light in a second wavelength band.

Abstract: An optical modulator of an optical transceiver can be calibrated using intermediate frequency (IF) signals to generate accurate crossing point values (e.g., DC bias). A photodiode can measure output from the optical modulator at intermediate and high-speed frequencies to generate crossing point values that avoid crossing point errors. A target crossing point can be selected at any value (e.g., 40%, 50%) and bias values can be generated from IF signals and then stored in a lookup date for setting the modulator bias during operation.

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 system for improved localization of image forgery. The system generates a variational information bottleneck objective function and works with input image patches to implement an encoder-decoder architecture. The encoder-decoder architecture controls an information flow between the input image patches and a representation layer. The system utilizes information bottleneck to learn useful residual noise patterns and ignore semantic content present in each input image patch. The system trains a neural network to learn a representation indicative of a statistical fingerprint of a source camera model from each input image patch while excluding semantic content thereof. The system can determine a splicing manipulation localization by the trained neural network.

Abstract: In a variety of embodiments, machine classifiers 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 generator including a hierarchical recurrent encoder-decoder network and the discriminator including a bi-directional recurrent neural network. The discriminator input may include a mixture of ground truth labels, the teacher-forcing outputs of the generator, and/or noise data. This mixture of input data may allow for richer feedback on the autoregressive outputs of the generator. The outputs can be ranked based on the discriminator feedback and a response selected from the ranked outputs.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a spatio-temporal embedding of a sequence of point clouds. One of the methods includes obtaining a temporal sequence comprising a respective point cloud input corresponding to each of a plurality of time points, each point cloud input comprising point cloud data generated from sensor data captured by one or more sensors of a vehicle at the respective time point; processing each point cloud input using a first neural network to generate a respective spatial embedding that characterizes the point cloud input; and processing the spatial embeddings of the point cloud inputs using a second neural network to generate a spatio-temporal embedding that characterizes the point cloud inputs in the temporal sequence.

Abstract: Embodiments described herein relate to a method, comprising: receiving input data at a convolutional neural network (CNN) model; generating a factorized computation network comprising a plurality of connections between a first layer of the CNN model and a second layer of the CNN model, wherein: the factorized computation network comprises N inputs, the factorized computation network comprises M outputs, and the factorized computation network comprises at least one path from every input of the N inputs to every output of the M outputs; setting a connection weight for a plurality of connections in the factorized computation network to 1 so that a weight density for the factorized computation network is <100%; performing fast pointwise convolution using the factorized computation network to generate fast pointwise convolution output; and providing the fast pointwise convolution output to the second layer of the CNN 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 comprise an integrated detection unit configured to record media content, identify preferred content, and communicate the identifications of preferred content for storage in a computationally efficient manner.

Abstract: Methods, systems, and apparatuses, including computer programs encoded on a computer storage medium, for machine learning classification based on separate processing of multiple views. In some implementations, a system obtains image data for multiple images showing different views of an object. A machine learning model is used to generate a separate output based on each the multiple images individually. The outputs for the respective images are combined to generate a combined output. A predicted characteristic of the object is determined based on the combined output. An indication of the predicted characteristic of the object is provided.

Abstract: In various examples, one or more deep neural networks (DNNs) are executed to regress on control points of a curve, and the control points may be used to perform a curve fitting operation—e.g., Bezier curve fitting—to identify landmark locations and geometries in an environment. The outputs of the DNN(s) may thus indicate the two-dimensional (2D) image-space and/or three-dimensional (3D) world-space control point locations, and post-processing techniques—such as clustering and temporal smoothing—may be executed to determine landmark locations and poses with precision and in real-time. As a result, reconstructed curves corresponding to the landmarks—e.g., lane line, road boundary line, crosswalk, pole, text, etc.—may be used by a vehicle to perform one or more operations for navigating an environment.

Abstract: This disclosure relates to methods, non-transitory computer readable media, and systems that can classify term sequences within a source text based on textual features analyzed by both an implicit-class-recognition model and an explicit-class-recognition model. For example, by applying machine-learning models for both implicit and explicit class recognition, the disclosed systems can determine a class corresponding to a particular term sequence within a source text and identify the particular term sequence reflecting the class. The dual-model architecture can equip the disclosed systems to apply (i) the implicit-class-recognition model to recognize implicit references to a class in source texts and (ii) the explicit-class-recognition model to recognize explicit references to the same class in source texts.

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: At least some embodiments of the present disclosure relate to a method of training an artificial neural network (ANN) for an artificial intelligence recognition. The method includes producing, by an ANN, outputs by feeding inputs of a training data set to the ANN; determining errors of the generated outputs from target outputs of the training data set; generating a first-order derivative matrix including first-order derivatives of the errors and a second-order derivative matrix including second-order derivatives of the errors; obtaining an approximation of the first-order derivative matrix or an approximation of the second-order derivative matrix by compressing the first-order derivative matrix or the second-order derivative matrix; and updating weights of the ANN based on the approximation of the first-order derivative matrix or the approximation of the second-order derivative matrix.

Abstract: An apparatus includes a sensor module. The sensor module includes an electromagnetic radiation sensor configured to provide electromagnetic radiation sensor data. The sensor module further includes a coded mask configured to modulate electromagnetic radiation incident to the electromagnetic radiation sensor and from which the electromagnetic radiation sensor data is generated. The apparatus further includes a computation module configured to obtain the electromagnetic radiation sensor data from the electromagnetic radiation sensor. The computation module is further configured to detect a property from the electromagnetic radiation sensor data using an artificial neural network. The computation module is further configured to output information related to the detected property via an output.

Abstract: Methods for augmenting a training image base representing a print on a background, for training parameters of a convolutional neural network, CNN, or for classification of an input image The present invention relates to a method for augmenting a training image base representing a print on a background, characterized in that it comprises the implementation, by data processing means (11) of a server (1), of steps of: (b) For at least a first image of said base, and a ridge map of a second print different from the print represented by said first image, generation by means of at least one generator sub-network (GB, GM, GLT) of a generative adversarial network, GAN, of a synthetic image presenting the background of said first image and representing the second print.

Abstract: The present disclosure provides an image processing method and apparatus, a storage medium and a terminal. The image processing method includes: acquiring a to-be-processed blurred image, wherein the to-be-processed blurred image is obtained by an under-screen camera through a device screen; inputting the to-be-processed blurred image to a trained generative adversarial network model to obtain a processed clear image, wherein the generative adversarial network model is trained using a preset training sample, the preset training sample includes a clear image sample and a blurred image sample corresponding to each other; and outputting the processed clear image. Embodiments of the present disclosure can improve image quality of an image captured by the under-screen camera.

Abstract: An image processing system includes: an image signal processor including a first neural network, and processing an input image by using the first neural network so as to generate a post-processed image; and a discriminator including a second neural network, and receiving a target image and the post-processed image, and discriminating the target image and the post-processed image into a real image and a fake image by using the second neural network, wherein the second neural network is trained to discriminate the target image as a real image and to discriminate the post-processed image as a fake image, and the first neural network is trained in such a manner that the post-processed image is discriminated as a real image by the second neural network.

Abstract: A computer-implemented method for implementing face recognition includes obtaining a face recognition model trained on labeled face data, separating, using a mixture of probability distributions, a plurality of unlabeled faces corresponding to unlabeled face data into a set of one or more overlapping unlabeled faces that include overlapping identities to those in the labeled face data and a set of one or more disjoint unlabeled faces that include disjoint identities to those in the labeled face data, clustering the one or more disjoint unlabeled faces using a graph convolutional network to generate one or more cluster assignments, generating a clustering uncertainty associated with the one or more cluster assignments, and retraining the face recognition model on the labeled face data and the unlabeled face data to improve face recognition performance by incorporating the clustering uncertainty.

Abstract: A method for jointly removing rolling shutter (RS) distortions and blur artifacts in a single input RS and blurred image is presented. The method includes generating a plurality of RS blurred images from a camera, synthesizing RS blurred images from a set of GS sharp images, corresponding GS sharp depth maps, and synthesized RS camera motions by employing a structure-and-motion-aware RS distortion and blur rendering module to generate training data to train a single-view joint RS correction and deblurring convolutional neural network (CNN), and predicting an RS rectified and deblurred image from the single input RS and blurred image by employing the single-view joint RS correction and deblurring CNN.