Abstract: A method for classifying objects which comprises a provision of measuring data from a sensor for a feature extraction unit as well as extraction of modality-independent features from the measuring data by means of the feature extraction unit, wherein the modality-independent features are independent of a sensor modality of the sensor, so that a conclusion to the sensor modality of the sensor is not possible from the modality-independent features.

Abstract: A method includes providing a computer system, the computer system including at least a processor and a memory, the memory including at least an operating system, executing a process in the memory, the process including providing a recurrent circuit model, and converting the recurrent circuit model into a recurrent neural network that can be fit with gradient descent.

Abstract: State of the art techniques in the domain of video analysis have limitations in terms of capability to capture spatio-temporal representation. This limitation in turn affects interpretation of video data. The disclosure herein generally relates to video analysis, and, more particularly, to a method and system for video analysis to capture spatio-temporal representation for video reconstruction and analysis. The method presents different architecture variations using three main deep network components: 2D convolution units, 3D convolution units and long short-term memory (LSTM) units for video reconstruction and analysis. These variations are trained for learning the spatio-temporal representation of the videos in order to generate a pre-trained video analysis module. By understanding the advantages and disadvantages of different architectural configurations, a novel architecture is designed for video reconstruction.

Abstract: A method for operating an artificial neural network (ANN) on a hardware platform. The ANN is designed to ascertain confidences with which input data are to be assigned to N discrete classes. The hardware platform includes a dedicated unit which forms from a list of M>N confidences expanded confidences by encoding into each confidence an identification number of its place in the list, and numerically sorts the expanded confidences. The unit is fed confidences 1, . . . , M?N, which have the minimal representable value, and confidences M?N+1, . . . , M, which correspond to the N discrete classes, and/or it is ensured that those confidences fed to the unit that correspond to one of the N discrete classes have a value higher than the minimal representable value. A ranking of the classes ordered according to confidences, to which the input data are to be assigned, is ascertained from the first N of the numerically sorted expanded confidences.

Abstract: A processing apparatus (10) including: an event extraction unit (11) that extracts, for each image of a time-series image group, an event in which a person and an object relate to each other; an event information generation unit (12) that generates event information indicating a time-series change of a first event defined by a first person, a first object, and a first relationship between a person and an object and also indicating a time-series change of a second event defined by a second person, a second object, and a second relationship between a person and an object; a causal relationship computation unit (13) that computes a score indicating a causal relationship between the first event and the second event; and an output unit (14) that outputs information indicating the first event and the second event for which the score satisfies a predetermined condition.

Abstract: An inpainting method includes obtaining image information at an electronic device, where the image information identifies an area corresponding to a removed object within an image. The method also includes reconstructing the area corresponding to the removed object by (i) applying a semantic mask and a surface normal map to identify and rank neighboring contexts of the area and (ii) sampling, using an attention mechanism, the ranked contexts to generate pixel information for the area. The method further includes rendering the image with the reconstructed area.

Abstract: An information processing apparatus includes one or more processors that function as the following units: a detection unit configured to detect a defect occurring in a structure by using local information about the structure based on a first image of the structure; a first determination unit configured to determine a global state of the structure based on a result of detection of the defect by the detection unit and global information indicating information about a wider area than an area of the structure indicated by the local information, the global information being based on a second image having a lower resolution than that of the first image; and a second determination unit configured to determine a degree of damage to a predetermined area of the structure based on a result of determination made by the first determination unit of the global state of the structure.

Abstract: An abnormality detection device is provided to detect an abnormal posture of a driver of a vehicle based on information of an image captured by an imaging unit for which an imaging region is defined to image a head of the driver. The abnormality detection device is configured to define at least one premonition region in a range in which the head is supposed to be positioned in process of transitioning from a normal posture to the abnormal posture in the imaging region. When the head stays in a premonition region, the abnormality detection device determines that the driver is in a state of the abnormal posture.

Abstract: In exemplary embodiments, methods and systems are provided for determining vehicle events. In an exemplary embodiment, a system is provided that includes: one or more cameras onboard a vehicle that are configured to generate camera data; and a processor that is coupled to the one or more cameras and configured to at least facilitate: identifying a change in position of one or more objects of the camera data using the camera data; and determining that a vehicle event has occurred, based on the change in position of the one or more objects of the camera data.

Abstract: Systems and methods for monitoring product placement. The method comprises accessing a first image depicting a plurality of items arranged in accordance with a first layout, accessing a second image, the second image depicting at least some of the plurality of items arranged in accordance with a second layout. The method then proceeds to inputting, to a machine learning algorithm (MLA), a first density map and a second density map, the first density map having been generated from the first image and the second density map having been generated from the second image. An anomaly map is then outputted by the ML, the anomaly map comprising a first indication of an item class associated with an anomaly and a second indication of a position associated with the anomaly.

Abstract: Methods and systems for predicting failure in a cyber-physical system include determining a prediction index based on a comparison of input time series, from respective sensors in a cyber-physical system, to failure precursors. A failure precursor is detected in the input time series, responsive to a comparison of the prediction index to a threshold. A subset of the sensors associated with the failure precursor is determined, based on a gradient of the prediction index. A corrective action is performed responsive to the determined subset of sensors.

Abstract: Disclosed are an artificial neural network device and a method of operating the same. The artificial neural network device includes an operation part performing an artificial neural network operation on an input feature map and a classification part performing a classifying operation on the input feature map based on the artificial neural network operation of the operation part. The operation part includes an XNOR operation circuit performing an XNOR operation on the input feature map and a filter and a binarizing circuit performing a binarization operation based on the result of the XNOR operation of the XNOR operation circuit. Accordingly, the artificial neural network device is miniaturized and performs the operation at high speed.

Abstract: Apparatus and methods for envelope tracking systems with automatic mode selection are provided herein. In certain configurations, a power amplifier system includes a power amplifier configured to provide amplification to a radio frequency signal and to receive power from a power amplifier supply voltage, and an envelope tracker including a signal bandwidth detection circuit configured to generate a detected bandwidth signal based on processing an envelope signal corresponding to an envelope of the radio frequency signal. The envelope tracker further includes a switch bank configured to receive a plurality of regulated voltages, a filter configured to filter an output of the switch bank to generate the power amplifier supply voltage, and a mode control circuit configured to control a filtering characteristic of the filter based on the detected bandwidth signal.

Abstract: Systems and methods for movement detection are provided. In one example embodiment, a computer-implemented method includes obtaining image data and range data representing a scene external to an autonomous vehicle, the image data including at least a first image and a second image that depict the scene. The method includes identifying a set of corresponding image features from the image data, the set of corresponding image features including a first feature in the first image having a correspondence with a second feature in the second image. The method includes determining a respective distance for each of the first feature and the second feature based at least in part on the range data. The method includes determining a velocity associated with a portion of a scene represented by the set of corresponding image features based at least in part on the respective distance for the first feature and the second feature.

Abstract: A method for optimizing a hardware structure of a convolutional neural network including: searching an initial feature value group by which a final convolution layer located at a final stage among a plurality of convolution layers, setting an initial fusing network by analyzing a feature value group having a size corresponding to the initial feature value group; computing an operation time for each layer by allocating the number of operators corresponding to the size of the feature value group to each of the plurality of convolution layers, and dividing the size of the feature value group by determining a layer having a minimum operation time; resetting the fusing network by changing the size of the feature value group, determining the size of the feature value group having the smallest number of operators, determining the number of operators to be included in each of the plurality of convolution layers.

Abstract: Embodiments described herein provide systems and processes for scene-aware object detection. This can involve an object detector that modulates its operations based on image location. The object detector can be a neural network detector or a scanning window detector, for example.

Abstract: A control apparatus is provided, including: an other-vehicle emotion acquiring unit configured to acquire an other-vehicle emotion indicating an emotion of an occupant of a second vehicle different from a first vehicle; a determination unit configured to determine whether to perform notification to an occupant of the first vehicle based on the other-vehicle emotion; and a notification control unit configured to perform control to notify the occupant of the first vehicle of notification information based on the other-vehicle emotion when the determination unit determines to perform the notification.

Abstract: An Artificial Intelligence (AI) roof deterioration analysis system that tracks changes in roofs over time by evaluating a series of features in high-resolution images.

Abstract: Front end systems and related devices, integrated circuits, modules, and methods are disclosed. One such front end system includes a low noise amplifier in a receive path and a multi-mode power amplifier circuit in a transmit path. An overload protection circuit can adjust an impedance of a switch coupled to the low noise amplifier based on a signal level of the low noise amplifier. The multi-mode power amplifier circuit includes a stacked output stage including a transistor stack of two or more transistors. The multi-mode power amplifier circuit also includes a bias circuit configured to control a bias of at least one transistor of the transistor stack based on a mode of the multi-mode power amplifier circuit. Other embodiments of front end systems are disclosed, along with related devices, integrated circuits, modules, methods, and components thereof.

Abstract: A learning apparatus for creating an emotion expression video according to an embodiment disclosed include first generative adversarial networks (GAN) that receive text for creating an emotion expression video, extract vector information by performing embedding on the input text, and create an image based on the extracted vector information, and second generative adversarial networks that receive an emotion expression image and a frame of comparison video, and create a frame of emotion expression video from the emotion expression image and the frame of comparison video.